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Zhang, Peng, Qin Qin, Shijie Zhang, et al. "Near Real-Time Remote Sensing Based on Satellite Internet: Architectures, Key Techniques, and Experimental Progress." Aerospace 11, no. 2 (2024): 167. http://dx.doi.org/10.3390/aerospace11020167.
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Remote sensing has become an essential tool for geological exploration, disaster monitoring, emergency rescue, and environmental supervision, while the limited number of remote sensing satellites and ground stations restricts the timeliness of remote sensing services. Satellite Internet has features of large bandwidth, low latency, and wide coverage, which can provide ubiquitous high-speed access for time-sensitive remote sensing users. This study proposes a near real-time remote sensing (NRRS) architecture, which allows satellites to transmit remote sensing data via inter-satellite links and offload to the Earth Stations from the satellite that moves overhead. The NRRS architecture has the advantages of instant response, ubiquitous access, and intelligent integration. Based on a test communication constellation, a vehicle-mounted Satcom on-the-move experiment was conducted to validate the presented NRRS architecture. The results show that the whole process from demand collection to image acquisition takes no more than 25 min, which provides an engineering reference for the subsequent implementation of near real-time remote sensing.
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Li, Xiuhong, Chongxiang Sun, Huilong Fan, and Jiale Yang. "Remote-Sensing Satellite Mission Scheduling Optimisation Method under Dynamic Mission Priorities." Mathematics 12, no. 11 (2024): 1704. http://dx.doi.org/10.3390/math12111704.
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Mission scheduling is an essential function of the management control of remote-sensing satellite application systems. With the continuous development of remote-sensing satellite applications, mission scheduling faces significant challenges. Existing work has many inherent shortcomings in dealing with dynamic task scheduling for remote-sensing satellites. In high-load and complex remote sensing task scenarios, there is low scheduling efficiency and a waste of resources. The paper proposes a scheduling method for remote-sensing satellite applications based on dynamic task prioritization. This paper combines the and Bound methodologies with an onboard task queue scheduling band in an active task prioritization context. A purpose-built emotional task priority-based scheduling blueprint is implemented to mitigate the flux and unpredictability characteristics inherent in the traditional satellite scheduling paradigm, improve scheduling efficiency, and fine-tune satellite resource allocation. Therefore, the Branch and Bound method in remote-sensing satellite task scheduling will significantly save space and improve efficiency. The experimental results show that comparing the technique to the three heuristic algorithms (GA, PSO, DE), the BnB method usually performs better in terms of the maximum value of the objective function, always finds a better solution, and reduces about 80% in terms of running time.
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Cheng, Yun, and Qiao Lin Huang. "Study on the Data Processing Technique in High Resolution Remote Sensing Satellite." Applied Mechanics and Materials 220-223 (November 2012): 2079–82. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2079.
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The development and application of high-resolution remote sensing was reviewed, the key problems which are confronted now were described, the main disparities between the research level home and abroad was analyzed. In the end, the prospect to the technique of improving image quality in high-resolution remote sensing satellite is given. Depending on self-operation of satellite system, accomplish the real-time image processing will be the trend of HR remote sensing in the future.
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Sangeetha.V1, Aishwarya.C.G Apoorva.T.M 2. "SATELLITE MULTISPECTRAL REMOTE SENSING IMAGE." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES [AIVESC-18] (April 26, 2018): 22–27. https://doi.org/10.5281/zenodo.1230360.
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The spectral classes of the imagery are finally translated into the different feature types in the image interpretation process (image processing). Presently, classification of all feature types is a manual process. Local and global climatic variability and change is inevitable which makes satellite imagery redundant in a short span of time. Due to the above stated reasons, we need an efficient and fast automatic feature extraction algorithm for better observing and organization of the resources of Earth. This paper is a study of different technique to extract urban built-up, land/vegetation and water features from Enhanced Thematic Mapper Plus (ETM+) (Landsat 7) imagery. The study selected three indices, Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), and Normalized Difference Vegetation Index (NDVI) to represent three major features on Earth: built-up land, open water body, and vegetation, respectively. Consequently, the seven bands of an original Landsat 7 image were reduced into three thematic-oriented bands derived from above indices, which were combined to compose a new image
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Ye, Fanghong, Tinghua Ai, Jiaming Wang, Yuan Yao, and Zheng Zhou. "A Method for Classifying Complex Features in Urban Areas Using Video Satellite Remote Sensing Data." Remote Sensing 14, no. 10 (2022): 2324. http://dx.doi.org/10.3390/rs14102324.
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The classification of optical satellite-derived remote sensing images is an important satellite remote sensing application. Due to the wide variety of artificial features and complex ground situations in urban areas, the classification of complex urban features has always been a focus of and challenge in the field of remote sensing image classification. Given the limited information that can be obtained from traditional optical satellite-derived remote sensing data of a classification area, it is difficult to classify artificial features in detail at the pixel level. With the development of technologies, such as satellite platforms and sensors, the data types acquired by remote sensing satellites have evolved from static images to dynamic videos. Compared with traditional satellite-derived images, satellite-derived videos contain increased ground object reflection information, especially information obtained from different observation angles, and can thus provide more information for classifying complex urban features and improving the corresponding classification accuracies. In this paper, first, we analyze urban-area, ground feature characteristics and satellite-derived video remote sensing data. Second, according to these characteristics, we design a pixel-level classification method based on the application of machine learning techniques to video remote sensing data that represents complex, urban-area ground features. Last, we conduct experiments on real data. The test results show that applying the method designed in this paper to classify dynamic, satellite-derived video remote sensing data can improve the classification accuracy of complex features in urban areas compared with the classification results obtained using static, satellite-derived remote sensing image data at the same resolution.
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Orlov, P. Yu, M. A. Boyarchuk, I. G. Zhurkin, and V. V. Nekrasov. "Development of geo-information technique and experimental studies on cross-calibration of Kanopus-V spacecraft series’ RSE sensors." Geodesy and Cartography 966, no. 12 (2021): 31–42. http://dx.doi.org/10.22389/0016-7126-2020-966-12-31-42.
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Cross-calibration of the Earth’s remote sensing payload is an addition to the traditionally used flight calibration. It consists of homogeneous terrain regions` image acquiring with a calibrated and reference apparatus and comparing the measured values of the spectral radiance. When selecting references for cross-calibration, the main requirements are the proximity of the spatial resolution and spectral channels of the satellite payload, as well as the observation conditions. Remote sensing spacecrafts Sentinel-2A / 2B and Landsat 8 were selected asreferences. An algorithm was developed to search for intersections of Earth remote sensing satellites ground tracks, which enables finding the parts of the Earth’s surface observed from satellites involved in calibration at a time difference not exceeding 30 minutes. Prediction of satellite paths is carried out using the analytical propagation model SGP4, and two-line element sets of orbital parameters (TLE) taken from open sources. Using the obtained intersection points of propagated ground tracks, the Kanopus-V grouping survey was planned and the corresponding materials by foreign systems were obtained. Basing on them, spectral radiance values obtained by calibrating satellites were compared showing the result of less than 10 % discrepancy.
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Rahman, Mohammad Mukhlesur, Mohammad Amirul Islam, Md Golam Mahboob, Nur Mohammad, and Istiak Ahmed. "FORECASTING OF POTATO YIELD ESTIMATION BY SATELLITE BASED REMOTE SENSING TECHNIQUE." Acta Informatica Malaysia 8, no. 2 (2024): 49–55. https://doi.org/10.26480/aim.02.2024.49.55.
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The goal of this research was to provide an operational technique with adequate technological components for monitoring and forecasting potato yield in Bangladesh. In the farmers’ fields of Shibganj upazila, the developed system investigates the combined use of satellite remote sensing (RS) and Geographic Information System (GIS) technology. The goal of the study was to construct a remotely sensed yield prediction model that used the high spatial resolution of Sentinel 2A and Landsat 8 satellite images to forecast potato yield one month ahead of harvest. Sentinel 2A (MSI) and Landsat 8 (OLI) satellite images with high spatial resolution data of 10m and 30m, respectively, were assessed, and 10-day and 16-day NDVI data were collected from these two satellites in this research. The study locations for the three potato growing seasons of 2018-2019, 2019-2020, and 2020-2021 were selected upazila. Sentinel 2A (MSI) and Landsat 8 (OLI) satellite data were used to get the NDVI values and yields for 20 farmers’ potato fields at Shibganj, Bogura district. The predicted percentages of the mean yield gap (or underestimation) for Sentinel 2A were 8.58%, while for Landsat 8, these were 9.56%, respectively, at Shibganj upazila, Bogura, during the potato growing season 2020–2021. The findings demonstrated that there is a substantial co-efficient of determination (R2 = 0.93 and 0.78) between remotely sensed NDVI and field-level potato yield. Therefore, remotely sensed NDVI data may be a useful tool for making early predictions about the yield of potatoes. Error of mean yield (%) of Sentinel 2A was better than Landsat 8 at Shibganj upazila, Bogura during the potato growing season 2020-2021.
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Hutapea, Destri Yanti, and Octaviani Hutapea. "WATERMARKING METHOD OF REMOTE SENSING DATA USING STEGANOGRAPHY TECHNIQUE BASED ON LEAST SIGNIFICANT BIT HIDING." International Journal of Remote Sensing and Earth Sciences (IJReSES) 15, no. 1 (2018): 63. http://dx.doi.org/10.30536/j.ijreses.2018.v15.a2824.
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Remote sensing satellite imagery is currently needed to support the needs of information in various fields. Distribution of remote sensing data to users is done through electronic media. Therefore, it is necessary to make security and identity on remote sensing satellite images so that its function is not misused. This paper describes a method of adding confidential information to medium resolution remote sensing satellite images to identify the image using steganography technique. Steganography with the Least Significant Bit (LSB) method is chosen because the insertion of confidential information on the image is performed on the rightmost bits in each byte of data, where the rightmost bit has the smallest value. The experiment was performed on three Landsat 8 images with different area on each composite band 4,3,2 (true color) and 6,5,3 (false color). Visually the data that has been inserted information does not change with the original data. Visually, the image that has been inserted with confidential information (or stego image) is the same as the original image. Both images cannot be distinguished on histogram analysis. The Mean Squared Error value of stego images of all three data less than 0.053 compared with the original image. This means that information security with steganographic techniques using the ideal LSB method is used on remote sensing satellite imagery.
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Udupi, Sachidananda K., H. C. Hemamalini, Vaibhav R. Chittora, D. K. Prabhuraj, and Siddanagouda Somanagouda Patil. "EFFICIENT SCHEMES OF CLASSIFIERS FOR REMOTE SENSING SATELLITE IMAGERIES OF LAND USE PATTERN CLASSIFICATIONS." Mercator 23, no. 2024 (2024): 1–10. http://dx.doi.org/10.4215/rm2024.e23004.
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Land use pattern classification of remote sensing imagery data is imperative to research that is used in remote sensing applications. Remote sensing (RS) technologies were exploited to mine some of the significant spatially variable factors, such as land cover and land use (LCLU), from satellite images of remote arid areas in Karnataka, India. Four diverse classification techniques unsupervised, and supervised (Maximum likelihood, Mahalnobis Distance, and Minimum Distance) are applied in Bellary district in Karnataka, India for the classification of the raw satellite images. The developed maps are then visually compared with each other and accuracy evaluations make using of ground-truths are carried out. It was initiated that the Maximum likelihood technique gave the finest results and both Minimum distance and Mahalnobis distance methods overvalued agricultural land areas. In spite of missing a few insignificant features due to the low resolution of the satellite images, a high-quality accord between parameters extracted automatically from the developed maps and field observations was found. Keywords: Remote sensing (RS), land cover and land use (LCLU).
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Emetere, M. E. "Modified satellite remote sensing technique for hydrocarbon deposit detection." Journal of Petroleum Science and Engineering 181 (October 2019): 106228. http://dx.doi.org/10.1016/j.petrol.2019.106228.
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An, Jintao, Tsz Ming Lu, Junhua Ma, and Tian Qiu. "Study on Regulation of Urban Heat Island Effect through Remote Sensing." Highlights in Science, Engineering and Technology 69 (November 6, 2023): 374–80. http://dx.doi.org/10.54097/hset.v69i.12138.
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Globally, the heat island effect is a major environmental problem that has a considerable impact on metropolitan climate, energy use, urban planning, and human health. So controlling the urban heat island effect is essential. The satellite remote sensing technology plays an essential role in observing and studying the urban heat island effect, providing critical scientific and technological support for its regulation. This article examines the fundamentals of regulating the urban heat island effect as well as the crucial function of remote sensing satellites. A summary of the advantages of using remote sensing satellites to observe and analyze the urban heat island effect is given, along with a study of the characteristics of popular remote sensing observation techniques. The possibility of further advancement and use of satellite remote sensing technologies in reducing the urban heat island effect has also been raised. Research on the control of the urban heat island effect may use this article as a reference.
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Bogireddy, Gari Sairekha. "An improved technique for enhancement of satellite image." i-manager’s Journal on Image Processing 11, no. 2 (2024): 10. http://dx.doi.org/10.26634/jip.11.2.20816.
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In the age of artificial intelligence, remote sensing and especially satellite imagery are gaining widespread interest among the computer science community in their efforts to enable machines to recognize their environment through satellite image classification. Imaging satellites provide images of Earth that are collected, analyzed, and processed for both civil and military purposes. Satellite images are an important source of data, captured by artificial satellites orbiting the Earth. These images are susceptible to noise and irregular illumination, which can affect their quality. This paper proposes an improved enhancement technique that increases the visual perception of the image while preserving its details. The proposed method uses image processing techniques with contrast enhancement to improve image quality. By enhancing contrast, this technique significantly benefits the creation of high-quality images. The effectiveness of the proposed method is evaluated using PSNR, entropy, and histogram analysis.
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Kulo, Nedim. "Different Methods for Remote Sensing Data Integration." Geodetski glasnik, no. 49 (December 31, 2018): 55–76. http://dx.doi.org/10.58817/2233-1786.2018.52.49.55.
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Nowadays remote sensing is an indispensable source of information about Earth's surface, primarily satellite-based remote sensing systems. Traditionally, the analysis of data collected from a particular area was based on the analysis of the data of one satellite image. The technological revolution improved spatial, temporal and radiometric resolution of satellite images, which allowed time datasets analysis, combining (integrating) data from various sensors, combining images of different scales and better integration with existing data and models. The integration of data from different sources is becoming an increasingly important factor in numerous aspects of remote sensing, and the results of this technique are used in solving everyday problems.
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Hu, Changmiao, and Ping Tang. "Rapid dehazing algorithm based on large-scale median filtering for high-resolution visible near-infrared remote sensing images." International Journal of Wavelets, Multiresolution and Information Processing 12, no. 05 (2014): 1461010. http://dx.doi.org/10.1142/s0219691314610104.
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In recent years, China's demand for satellite remote sensing images increased. Thus, the country launched a series of satellites equipped with high-resolution sensors. The resolutions of these satellites range from 30 m to a few meters, and the spectral range covers the visible to the near-infrared band. These satellite images are mainly used for environmental monitoring, mapping, land surface classification and other fields. However, haze is an important factor that often affects image quality. Thus, dehazing technology is becoming a critical step in high-resolution remote sensing image processing. This paper presents a rapid algorithm for dehazing based on a semi-physical haze model. Large-scale median filtering technique is used to extract large areas of bright, low-frequency information from images to estimate the distribution and thickness of the haze. Four images from different satellites are used for experiment. Results show that the algorithm is valid, fast, and suitable for the rapid dehazing of numerous large-sized high-resolution remote sensing images in engineering applications.
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Gottapu Santosh Kumar, Et al. "An Overview of Deep Learning Networks for Remote Sensing Applications." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 1509–12. http://dx.doi.org/10.17762/ijritcc.v11i10.8701.
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To study and understand the world around us, remote sensing specialists rely on aerial and satellite photographs. Today, deep learning models necessitating extensive data or specialised data are employed in many remote sensing applications. Sometimes, the spatial and spectral resolution of Observation satellites of the planet earth will fall short of requirements due to technological constraints in optics and sensors, as well as the expensive expense of upgrading sensors and equipment. Insufficient information might reduce a model's efficiency. The efficiency of deep learning frameworks that rely on data can be improved by the use of a adversarial networks, which is a type of technique that can generate synthetic data. This is one of the best innovative developments in Deep Learning in past decade. GANs have seen rapid adoption and widespread success in the Remote Sensing sector. GANs can also perform picture-to-image translation, such as clearing cloud cover from a satellite image.This paper aims to investigate the applications of different Adversarial Networks in the remote sensing area and the databases used for training of GANs and metrics of evaluation.
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Liu, Jian, and Sheng Feng Zhu. "Primary Studies on the Offshore Oil Spill Detection System Using the Satellite Remote Sensing Technology Developed by China National Offshore Oil Corporation." Applied Mechanics and Materials 316-317 (April 2013): 580–85. http://dx.doi.org/10.4028/www.scientific.net/amm.316-317.580.
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Oil spill detection has important significance for the oceanic environmental protection. With the rapid development of the satellite remote sensing, remote sensing technique has become one of the important and effective tools in oil spill detection. This paper discussed the method of the offshore surface oil spill detection using Synthetic Aperture Radar (SAR). The oil spill detection systems used at home and abroad is evaluated. Finally, the feasibility of the oil spill detection system based on the satellite remote sensing developed by China National Offshore Oil Corporation is studied.
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Dong, L., S. Lyu, L. Wang, and X. Gao. "RESEARCH ON COOPERATION STRATEGY BASED ON SATELLITE REMOTE SENSING DATA SERVICE AND TECHNOLOGY APPLICATION BETWEEN CHINA AND ASEAN." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 13, 2023): 1373–78. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-1373-2023.
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Abstract. Remote sensing (RS) and earth observing technology are flourished with the development of a series of high-resolution earthobservation satellites. As the improvement of China’s earth observation data acquisition capability, one critical issue is put on the agenda that is what kind of models and techniques can promote the future data processing into a new level in terms of service model, massive data processing, development methods, business models, resource sharing, and system sustainability (1). In order to embed domestic satellite advantages into global world and provide increasingly more Chinese wisdom and solution to support the ASEAN regional development, China highlights the propositions on ASEAN-China remote sensing cooperation. On November 1, 2022, the ASEAN-China Satellite Remote Sensing Application Centre (hereinafter referred to as the ACSAC) was officially inaugurated in Beijing. The ACSAC will mainly focus on the establishment of a system and mechanism for a series of substantive operation, promoting the comprehensive sharing of China's land and ocean satellite data, comprehensively rich the international application scenarios of Chinese satellites, promoting applications in multiple fields and carrying out satellite remote sensing application promotion and typical demonstration, continuing to carry out technical exchanges and training with mutual demands and development. At present, ACSAC has stepped into the substantive construction stage. It will give full play to the advantages of China's natural resources land and ocean satellite remote sensing resources, to form a multi-scale, full coverage data resource support, jointly carry out the construction of the ASEAN-China satellite remote sensing data network platform based on the cloud environment, and to jointly build an integrated service open portal for land and ocean satellite data products to ASEAN region. Focusing on the core construction content of ACSAC, this paper systematically collects, summarizes and analyse the data and application needs of satellite remote sensing in ASEAN countries, finds out the current needs to meet the gap, and puts forward some thoughts and ideas on cooperation mechanism, data sharing, customized products and application demonstration of advanced products, aiming to lay a solid foundation for the substantial construction of ACSAC.
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Palani, Murugan, Lakshmi Gomathi, and Kumar Gautam Vivek. "High Resolution Optical Remote Sensing Satellites - Challenges and Techniques." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 495–502. https://doi.org/10.35940/ijeat.E9670.069520.
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High spatial resolution satellite data is essential to identify small objects and extract minute details of the terrain. This data is provided by many satellites and being used in numerous applications. The realization of high resolution satellite is a challenging task. Significant complexity lies in the realization of high spatial resolution camera starting from material selection, high stiffness-low mass opto-mechanical system design, detector selection to high speed camera electronics design. The mass and size of camera increase with the improvement in spatial resolution. Alternate methods such as Step and Stare, and time delay integration methods can be used to achieve high signal to noise ratio images. The performance of satellite bus subsystems like structure, the data handling, and storage system, the data transmission system, attitude sensors and actuators should also be improved to achieve good quality data. The data handling system has to be designed to handle high data rate and data volume. The capacity of the data storage system has to be increased to cater the high data volume storage requirement. The data transmission system needs to be sufficiently capable to transmit the high volume imaging data to the ground station. As the spatial resolution improves, the spacecraft pointing accuracy and drift rate requirements become stringent. Improved attitude sensors and high capacity actuators are essential to meet these stringent requirements. Generally, high resolution cameras are combined with high speed electronics to handle high data rates which need more power. In this paper, we discuss various techniques being employed to obtain high resolution data with reasonable SNR. The challenges involved and the improvements required in various spacecraft subsystems to support these high resolution cameras are presented. Techniques employed by different space agencies to obtain high spatial resolution images are also discussed. The characteristics of high resolution satellites are also tabulated and compared.
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Andrés-Anaya, Paula, Adolfo Molada-Tebar, David Hernández-López, Miguel Ángel Moreno, Diego González-Aguilera, and Mónica Herrero-Huerta. "Radiometric Improvement of Spectral Indices Using Multispectral Lightweight Sensors Onboard UAVs." Drones 8, no. 2 (2024): 36. http://dx.doi.org/10.3390/drones8020036.
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Close-range remote sensing techniques employing multispectral sensors on unoccupied aerial vehicles (UAVs) offer both advantages and drawbacks in comparison to traditional remote sensing using satellite-mounted sensors. Close-range remote sensing techniques have been increasingly used in the field of precision agriculture. Planning the flight, including optimal flight altitudes, can enhance both geometric and temporal resolution, facilitating on-demand flights and the selection of the most suitable time of day for various applications. However, the main drawbacks stem from the lower quality of the sensors being used compared to satellites. Close-range sensors can capture spectral responses of plants from multiple viewpoints, mitigating satellite remote sensing challenges, such as atmospheric interference, while intensifying issues such as bidirectional reflectance distribution function (BRDF) effects due to diverse observation angles and morphological variances associated with flight altitude. This paper introduces a methodology for achieving high-quality vegetation indices under varied observation conditions, enhancing reflectance by selectively utilizing well-geometry vegetation pixels, while considering factors such as hotspot, occultation, and BRDF effects. A non-parametric ANOVA analysis demonstrates significant statistical differences between the proposed methodology and the commercial photogrammetric software AgiSoft Metashape, in a case study of a vineyard in Fuente-Alamo (Albacete, Spain). The BRDF model is expected to substantially improve vegetation index calculations in comparison to the methodologies used in satellite remote sensing and those used in close-range remote sensing.
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Roy, P. S., M. D. Behera, and S. K. Srivastav. "Satellite Remote Sensing: Sensors, Applications and Techniques." Proceedings of the National Academy of Sciences, India Section A: Physical Sciences 87, no. 4 (2017): 465–72. http://dx.doi.org/10.1007/s40010-017-0428-8.
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Wagh, Santosh, and Vivek Manekar. "Assessment of Reservoir Sedimentation using Satellite Remote Sensing Technique (SRS)." Journal of The Institution of Engineers (India): Series A 102, no. 3 (2021): 851–60. http://dx.doi.org/10.1007/s40030-021-00539-8.
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Fraser, A., P. Huggins, J. Rees, and P. Cleverly. "A satellite remote sensing technique for geological structure horizon mapping." International Journal of Remote Sensing 18, no. 7 (1997): 1607–15. http://dx.doi.org/10.1080/014311697218313.
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Roohi, Mohammad. "Enhancing the Quality of Satellite Images for Estimating the Water Body." JSM Environmental Science and Ecology 12, no. 1 (2024): 1–9. http://dx.doi.org/10.47739/2333-7141.environmentalscience.1088.
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Abstract Water resources are indeed limited, and factors such as drought, climate change, and human activities can contribute to their decrease. To estimate the amount of water stored in a dam lake, several methods can be employed. Remote sensing techniques, such as satellite imagery can be used to estimate the water surface area of the reservoir. In this study, the amount of water cover changes is investigated using a remote sensing technique. Also, to increase the level of accuracy in estimating the water cover of the dam lake, the technique of Image Fusion Landsat-8 satellites images and increasing the level of spatial accuracy from 30 meters to 15 meters has been used. In this study, it has been shown that by combining Landsat satellite images, the accuracy in calculating and estimating the water cover area has increased. Also, in this study, the depth changes between 2017-2020 have been investigated. In the research conducted, the amount of water stored in the dam lake has decreased due to the droughts of the last few years, due to the increase in water consumption in the urban and agricultural areas, especially during the drought period.
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Tang, Qiuhong, Huilin Gao, Hui Lu, and Dennis P. Lettenmaier. "Remote sensing: hydrology." Progress in Physical Geography: Earth and Environment 33, no. 4 (2009): 490–509. http://dx.doi.org/10.1177/0309133309346650.
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Satellite remote sensing is a viable source of observations of land surface hydrologic fluxes and state variables, particularly in regions where in situ networks are sparse. Over the last 10 years, the study of land surface hydrology using remote sensing techniques has advanced greatly with the launch of NASA’s Earth Observing System (EOS) and other research satellite platforms, and with the development of more sophisticated retrieval algorithms. Most of the constituent variables in the land surface water balance (eg, precipitation, evapotranspiration, snow and ice, soil moisture, and terrestrial water storage variations) are now observable at varying spatial and temporal resolutions and accuracy via remote sensing. We evaluate the current status of estimates of each of these variables, as well as river discharge, the direct estimation of which is not yet possible. Although most of the constituent variables are observable by remote sensing, attempts to close the surface water budget from remote sensing alone have generally been unsuccessful, suggesting that current generation sensors and platforms are not yet able to provide hydrologically consistent observations of the land surface water budget at any spatial scale.
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Luo, Xin, Maocai Wang, Guangming Dai, and Xiaoyu Chen. "A Novel Technique to Compute the Revisit Time of Satellites and Its Application in Remote Sensing Satellite Optimization Design." International Journal of Aerospace Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/6469439.
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This paper proposes a novel technique to compute the revisit time of satellites within repeat ground tracks. Different from the repeat cycle which only depends on the orbit, the revisit time is relevant to the payload of the satellite as well, such as the tilt angle and swath width. The technique is discussed using the Bezout equation and takes the gravitational second zonal harmonic into consideration. The concept of subcycles is defined in a general way and the general concept of “small” offset is replaced by a multiple of the minimum interval on equator when analyzing the revisit time of remote sensing satellites. This technique requires simple calculations with high efficiency. At last, this technique is used to design remote sensing satellites with desired revisit time and minimum tilt angle. When the side-lap, the range of altitude, and desired revisit time are determined, a lot of orbit solutions which meet the mission requirements will be obtained fast. Among all solutions, designers can quickly find out the optimal orbits. Through various case studies, the calculation technique is successfully demonstrated.
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Murugan, Palani, Vivek Kumar Gautam, and V. Ramanathan. "Performance evaluation of super resolution algorithms in generating high resolution images using MSE and PSNR." International Journal of Engineering and Computer Science 10, no. 02 (2021): 25284–91. http://dx.doi.org/10.18535/ijecs/v10i02.4560.
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In recent days, requirement of high spatial resolution remote sensing data in various fields has increased tremendously. High resolution satellite remote sensing data is obtained with long focal length optical systems and low altitude. As fabrication of high-resolution optical system and accommodating on the satellite is a challenging task, various alternate methods are being explored to get high resolution imageries. Alternately the high-resolution data can be obtained from super resolution techniques. The super resolution technique uses single or multiple low-resolution mis-registered data sets to generate high resolution data set. Various algorithms are employed in super resolution technique to derive high spatial resolution. In this paper we have compared two methods namely overlapping and interleaving methods and their capability in generating high resolution data are presented.
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Scheibenreif, L., M. Mommert, and D. Borth. "CONTRASTIVE SELF-SUPERVISED DATA FUSION FOR SATELLITE IMAGERY." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2022 (May 17, 2022): 705–11. http://dx.doi.org/10.5194/isprs-annals-v-3-2022-705-2022.
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Abstract. Self-supervised learning has great potential for the remote sensing domain, where unlabelled observations are abundant, but labels are hard to obtain. This work leverages unlabelled multi-modal remote sensing data for augmentation-free contrastive self-supervised learning. Deep neural network models are trained to maximize the similarity of latent representations obtained with different sensing techniques from the same location, while distinguishing them from other locations. We showcase this idea with two self-supervised data fusion methods and compare against standard supervised and self-supervised learning approaches on a land-cover classification task. Our results show that contrastive data fusion is a powerful self-supervised technique to train image encoders that are capable of producing meaningful representations: Simple linear probing performs on par with fully supervised approaches and fine-tuning with as little as 10% of the labelled data results in higher accuracy than supervised training on the entire dataset.
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Lodwick, G. D., and S. H. Paine. "SATELLITE REMOTE SENSING IN SURVEYING PRESENT OPPORTUNITIES, FUTURE POSSIBILITIES." Canadian Surveyor 40, no. 3 (1986): 315–26. http://dx.doi.org/10.1139/tcs-1986-0025.
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Of all the areas of the earth sciences affected by satellite remote sensing, the surveying profession has been one of the last to take advantage of its unique features. This is due in part to: resolution limitations of Landsat 1, 2 and 3, difficulties in registration and positioning of the imagery, technical constraints in handling vast quantities of digital data, and the excellent methods currently available for the production of cartographic products. Nevertheless, satellite remote sensing has now emerged as a complementary procedure to many existing techniques utilized in surveying and mapping. Already, Landsat is being used for topographic mapping, hydrographic surveying and resource mapping purposes. However, with the improved resolution of Landsat 4, the potential of stereoscopic coverage with the SPOT satellites and present developments in computer processing and data manipulation, satellite remote sensing in the next decade will emerge as an indispensable tool for mapping and cartography.
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Zhang, F., Z. Zhang, L. Yan, et al. "ADVANCES IN OPTICAL POLARIZATION REMOTE SENSING FOR MARINE OBSERVATION: A CASE STUDY IN NANCHANG RIVER PARK." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2022 (May 17, 2022): 101–6. http://dx.doi.org/10.5194/isprs-annals-v-3-2022-101-2022.
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Abstract. Marine observation is a worldwide challenge, which implicates for a large number of social, economic and scientific problems. Satellite remote sensing provides incredible convenience for marine observation, and remote sensing techniques with different wavelength range have been developed for scientific use related to oceanography, among of which optical polarization remote sensing is a rapidly growing field in the recent decade. Although some attempts have been made about utilizing optical polarization technique for marine observation, the potential of optical polarization remote sensing is far from being fully released and the current skills of optical polarization image processing are too coarse to extract deep information from raw images. In our experiment at Nanchang river park, three application scenarios are selected to illustrate advances in optical polarization remote sensing for marine observation, specifically including sun-glint observation, phytoplankton monitoring and coastal topography mapping. A baseline for optical polarization image processing is established for marine observation and the advantages of optical polarization technique are assessed qualitatively and quantitatively, proving that: For marine observation, optical polarization remote sensing can reduce overexposure rate, enhance dynamic range, depict subsurface phytoplankton and map coastal topography.
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Mohamed Ali, Abbas Sayed Ahmed, and Ahmed Abu Al Qasim Al Hassan. "Remote Sensing and Its Uses in Archeology." Journal of Arts and Social Sciences [JASS] 2, no. 1 (2011): 5. http://dx.doi.org/10.24200/jass.vol2iss1pp5-25.
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Aerial photography, remote sensing technique has been used as a tool for acquisition of archaeological information for several decades. At the turn of the twentieth century, archaeologists realized that valuable archaeological data could be extracted from aerial photos, thus it has been developed into a systematic discipline known as aerial archaeology. Though aerial photography has a long history of use, Satellite remote sensing is a recent discipline applied in detection, mapping and analysis of archaeological matter, providing that the spatial resolution of the sensor is adequate to detect the features. Both aerial photography and satellite imagery have advantages and limitations with regard to archaeological applications. In the last few years, combination of the two was found to be ideal for archaeological remote sensing applications. Remote sensing has increased in importance to archaeology, as being an important close integrator with Geographic Information Systems. Remote sensing and its kindred tool of GIS have become central elements of modern spatial information and analysis system in archaeology.
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Mohamed Ali, Abbas Sayed Ahmed, and Ahmed Abu Al Qasim Al Hassan. "Remote Sensing and Its Uses in Archeology." Journal of Arts and Social Sciences [JASS] 2, no. 1 (2011): 5–25. http://dx.doi.org/10.53542/jass.v2i1.1032.
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Aerial photography, remote sensing technique has been used as a tool for acquisition of archaeological information for several decades. At the turn of the twentieth century, archaeologists realized that valuable archaeological data could be extracted from aerial photos, thus it has been developed into a systematic discipline known as aerial archaeology. Though aerial photography has a long history of use, Satellite remote sensing is a recent discipline applied in detection, mapping and analysis of archaeological matter, providing that the spatial resolution of the sensor is adequate to detect the features. Both aerial photography and satellite imagery have advantages and limitations with regard to archaeological applications. In the last few years, combination of the two was found to be ideal for archaeological remote sensing applications. Remote sensing has increased in importance to archaeology, as being an important close integrator with Geographic Information Systems. Remote sensing and its kindred tool of GIS have become central elements of modern spatial information and analysis system in archaeology.
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Pershin, Sergey M., Boris G. Katsnelson, Mikhail Ya Grishin, Vasily N. Lednev, Vladimir A. Zavozin, and Ilia Ostrovsky. "Laser Remote Sensing of Lake Kinneret by Compact Fluorescence LiDAR." Sensors 22, no. 19 (2022): 7307. http://dx.doi.org/10.3390/s22197307.
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Harmful algal blooms in freshwater reservoirs became a steady phenomenon in recent decades, so instruments for monitoring water quality in real time are of high importance. Modern satellite remote sensing is a powerful technique for mapping large areas but cannot provide depth-resolved data on algal concentrations. As an alternative to satellite techniques, laser remote sensing is a perspective technique for depth-resolved studies of fresh or seawater. Recent progress in lasers and electronics makes it possible to construct compact and lightweight LiDARs (Light Detection and Ranging) that can be installed on small boats or drones. LiDAR sensing is an established technique; however, it is more common in studies of seas rather than freshwater reservoirs. In this study, we present an experimental verification of a compact LiDAR as an instrument for the shipborne depth profiling of chlorophyll concentration across the freshwater Lake Kinneret (Israel). Chlorophyll depth profiles of 3 m with a 1.5 m resolution were measured in situ, under sunlight conditions. A good correlation (R2 = 0.89) has been established between LiDAR signals and commercial algae profiler data. A non-monotonic algae depth distribution was observed along the boat route during daytime (Tiberias city–Jordan River mouth–Tiberias city). The impact of high algal concentration on water temperature laser remote sensing has been studied in detail to estimate the LiDAR capability of in situ simultaneous measurements of temperature and chlorophyll concentration.
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Anand, Sakshi, and Rakesh Sharma. "Pansharpening and spatiotemporal image fusion method for remote sensing." Engineering Research Express 6, no. 2 (2024): 022201. http://dx.doi.org/10.1088/2631-8695/ad3a34.
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Abstract In last decades, remote sensing technology has rapidly progressed, leading to the development of numerous earth satellites such as Landsat 7, QuickBird, SPOT, Sentinel-2, and IKONOS. These satellites provide multispectral images with a lower spatial resolution and panchromatic images with a higher spatial resolution. However, satellite sensors are unable to capture images with high spatial and spectral resolutions simultaneously due to storage and bandwidth constraints, among other things. Image fusion in remote sensing has emerged as a powerful tool for improving image quality and integrating important features from multiple source images into one, all while maintaining the integrity of critical features. It is especially useful for high-resolution remote sensing applications that need to integrate features from multiple sources and hence a vital pre-processing step for various applications, including medical, computer vision, and satellite imaging. This review initially gives a basic framework for image fusion, followed by statistical analysis and a comprehensive review of various state-of-the-art image fusion methods, where they are classified based on the number of sensors used, processing levels, and type of information being fused. Subsequently, a thorough analysis of STF and pansharpening techniques for remote sensing applications has been covered, where the dataset of the DEIMOS-2 satellite is employed for evaluating various pansharpening methods while MODIS and Landsat images are employed in the spatiotemporal fusion method. A comparative evaluation of several approaches has been carried out to assess the merits and drawbacks of the current approaches. Several real-time applications of remote sensing image fusion have been explored, and current and future directions in fusion research for remote sensing have been discussed, along with the obstacles they present.
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Manikiam, Balakrishnan. "Applications of IRS and INSAT Data with Specific Case Studies." Mapana - Journal of Sciences 13, no. 1 (2017): 85–99. http://dx.doi.org/10.12723/mjs.28.6.
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Indian satellite programme has over the past three decades achieved operational capability in the area of remote sensing. The Indian Remote Sensing (IRS) satellites are developed towards providing data for natural resources survey and management. Techniques have been developed to retrieve several parameters related to land, ocean and atmosphere. Since the launch of IRS 1A in early 80’s, the technology has improved to achieve satellite imagery with resolution of 1 meter. The Indian National satellite (INSAT) system is made up of geostationary satellites towards monitoring and study of weather over the Indian region. The INSAT data is operationally used for study of monsoon onset, cyclone prediction and forecast of severe weather conditions. The paper portrays a few unique case studies using IRS and INSAT data. The satellite data is proving to be very useful in study of the global changes and possible impacts.
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Xiang, Jiahao, Yujie Xie, and Siyu Zhou. "The Application of Remote Sensing-Based Technology in The Field of Tea Identification and Distribution." Transactions on Environment, Energy and Earth Sciences 3 (November 26, 2024): 239–45. https://doi.org/10.62051/rybj6393.
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As a significant economic crop cultivated and consumed globally, the yield and quality of tea are directly correlated with the stability and growth of the international tea market. The application of remote sensing technology enables the precise monitoring of tea plant growth, the real-time assessment of soil moisture and nutrient distribution, and the identification of pests and diseases. This technology facilitates the implementation of scientific management practices, thereby enhancing the yield and quality of tea. This paper begins by providing an overview of the remote sensing data sources that can be used for tea monitoring. It then selects two newer remote sensing methods and discusses their potential applications to tea plantations in West Lake, Hangzhou and Bangladesh. There are numerous categories of remote sensing data sources, including satellite remote sensing data and unmanned aerial vehicle (UAV) low-altitude imagery. In the initial case study, the HRNetV2 base deep learning model was employed to detect Longjing tea in West Lake, Hangzhou. The technique integrated satellite remote sensing data with a machine learning model, resulting in a relatively low error rate. The second case study delves into assessing Bangladesh's suitability for sustainable tea land production, leveraging an expert system with satellite remote sensing data and Geographic Information Systems (GIS). This integrated research methodology presents a holistic, precise, and trustworthy framework indispensable for propelling the progression of the tea industry within the country's context.
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Chen, Yizhou. "Application Of Spatio-Temporal Remote Sensing Data Analysis in Fire Monitoring." Transactions on Environment, Energy and Earth Sciences 3 (November 26, 2024): 26–31. https://doi.org/10.62051/14b9fc20.
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This paper investigates the application of spatio-temporal remote sensing data analysis in fire monitoring, aiming to cope with the increase in the frequency of forest fires and its threat to the ecological environment and human security due to global warming and increased human activities. The study describes the application of various remote sensing techniques in fire monitoring, including Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data, infrared remote sensing, satellite hyperspectral data, and SAR techniques. The application of remote sensing data in actual fire monitoring was demonstrated through case studies of the "330 forest fire" in Muli County, Sichuan Province, and the boreal forest fire in Canada. The integration of multi-source satellite remote sensing data can improve the timeliness of monitoring and avoid the interference of complex environments, thus reducing disaster losses. This paper argues that remote sensing technology has a broad development prospect in forest fire monitoring and that the accuracy and timeliness of monitoring can be improved by further integrating GIS, innovative technologies, and algorithmic applications. The future challenge lies in strengthening the integration of remote sensing technology and GIS to enhance data processing capability and disaster prediction accuracy to provide more effective support and assurance.
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He, Yan, Kebin Jia, and Zhihao Wei. "Improvements in Forest Segmentation Accuracy Using a New Deep Learning Architecture and Data Augmentation Technique." Remote Sensing 15, no. 9 (2023): 2412. http://dx.doi.org/10.3390/rs15092412.
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Forests are critical to mitigating global climate change and regulating climate through their role in the global carbon and water cycles. Accurate monitoring of forest cover is, therefore, essential. Image segmentation networks based on convolutional neural networks have shown significant advantages in remote sensing image analysis with the development of deep learning. However, deep learning networks typically require a large amount of manual ground truth labels for training, and existing widely used image segmentation networks struggle to extract details from large-scale high resolution satellite imagery. Improving the accuracy of forest image segmentation remains a challenge. To reduce the cost of manual labelling, this paper proposed a data augmentation method that expands the training data by modifying the spatial distribution of forest remote sensing images. In addition, to improve the ability of the network to extract multi-scale detailed features and the feature information from the NIR band of satellite images, we proposed a high-resolution forest remote sensing image segmentation network by fusing multi-scale features based on double input. The experimental results using the Sanjiangyuan plateau forest dataset show that our method achieves an IoU of 90.19%, which outperforms prevalent image segmentation networks. These results demonstrate that the proposed approaches can extract forests from remote sensing images more effectively and accurately.
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Xie, Songlin, Lei Zhang, Gwanggil Jeon, and Xiaomin Yang. "Remote Sensing Neural Radiance Fields for Multi-View Satellite Photogrammetry." Remote Sensing 15, no. 15 (2023): 3808. http://dx.doi.org/10.3390/rs15153808.
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Neural radiance fields (NeRFs) combining machine learning with differentiable rendering have arisen as one of the most promising approaches for novel view synthesis and depth estimates. However, NeRFs only applies to close-range static imagery and it takes several hours to train the model. The satellites are hundreds of kilometers from the earth. Satellite multi-view images are usually captured over several years, and the scene of images is dynamic in the wild. Therefore, multi-view satellite photogrammetry is far beyond the capabilities of NeRFs. In this paper, we present a new method for multi-view satellite photogrammetry of Earth observation called remote sensing neural radiance fields (RS-NeRFs). It aims to generate novel view images and accurate elevation predictions quickly. For each scene, we train an RS-NeRF using high-resolution optical images without labels or geometric priors and apply image reconstruction losses for self-supervised learning. Multi-date images exhibit significant changes in appearance, mainly due to cars and varying shadows, which brings challenges to satellite photogrammetry. Robustness to these changes is achieved by the input of solar ray direction and the vehicle removal method. NeRFs make it intolerable by requiring a very long time to train an easy scene. In order to significantly reduce the training time of RS-NeRFs, we build a tiny network with HashEncoder and adopted a new sampling technique with our custom CUDA kernels. Compared with previous work, our method performs better on novel view synthesis and elevation estimates, taking several minutes.
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Deng, Yi, Chengyue Xing, and Ling Cai. "Building Image Feature Extraction Using Data Mining Technology." Computational Intelligence and Neuroscience 2022 (April 13, 2022): 1–12. http://dx.doi.org/10.1155/2022/8006437.
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At present, data mining technology is continuously researched in science and application. With the rapid development of remote sensing satellite industry, especially the launch of remote sensing satellites with high-resolution sensors, the amount of information obtained from remote sensing images has increased dramatically, which has largely promoted the application of remote sensing data in various industries. This technique mines useable information from less complete and accurate data while ensuring low program complexity. In order to determine the impact of data mining techniques on feature extraction of graphic images, this paper explores the relevant steps in the image recognition process, especially the image preenhancement and image extraction processes. This paper develops a preliminary set of relevant data and investigates two different extraction methods based on the availability or absence of nursing information. Aiming at the advantages and disadvantages of the two house extraction methods, this work discusses how to effectively integrate remote sensing data. It uses different data sources to describe different characteristics of buildings, analyzes and extracts effective information, and finally derives building information. The research results show that, using the SVM algorithm in data mining for image feature extraction, in the verified filtering window, the accuracy can be effectively improved by about 20%. Buildings are important objects in high-resolution remote sensing images, and their feature extraction and recognition technology is of great significance in many fields such as digital city construction, urban planning, and military reconnaissance.
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Musali, Suresh Kumar, Rajeshwari Janthakal, and Nuvvusetty Rajasekhar. "Deep learning techniques for satellite image classification." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 3 (2025): 1712. https://doi.org/10.11591/ijeecs.v37.i3.pp1712-1725.
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Because of its wide range of uses in computer vision applications, including image retrieval, remote sensing, object recognition, scene analysis, and surveillance, image classification has attracted a lot of attention. Assigning appropriate class labels to images according to their contents is the primary objective of image classification. In the domain of remote sensing, image classification and analysis play crucial roles in both military and civil applications. Conventional methods for scene analysis and remote sensing depended on low-level representations of features, such as those of color and texture. However, recent advancements have shifted towards the use of convolutional neural networks (CNNs), which have shown promising results in remote sensing and scene classification tasks. In light of effectiveness of deep learning (DL) models, this research aims to develop four DL models by fine tuning already existing DL models-CNNs, residual neural network (ResNet), visual geometry group (VGG-19), network mobile net V2 based model and classifies satellite images of RSI-CB256 data set in to four classes namely cloudy, desert, green_area and water. For the RSI-CB256 dataset, appropriate network structures are explored in this research to get good performance. The CNN, ResNet and VGG-19 base models achieved an accuracy of 90.48, 92.68 and 91.18 respectively. While the mobile net V2 based model outperformed the other three models with 96.83% accuracy.
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Suresh, Kumar Musali Rajeshwari Janthakal Nuvvusetty Rajasekhar. "Deep learning techniques for satellite image classification." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 3 (2025): 1712–25. https://doi.org/10.11591/ijeecs.v37.i3.pp1712-1725.
Full textAbstract:
Because of its wide range of uses in computer vision applications, including image retrieval, remote sensing, object recognition, scene analysis, and surveillance, image classification has attracted a lot of attention. Assigning appropriate class labels to images according to their contents is the primary objective of image classification. In the domain of remote sensing, image classification and analysis play crucial roles in both military and civil applications. Conventional methods for scene analysis and remote sensing depended on low-level representations of features, such as those of color and texture. However, recent advancements have shifted towards the use of convolutional neural networks (CNNs), which have shown promising results in remote sensing and scene classification tasks. In light of effectiveness of deep learning (DL) models, this research aims to develop four DL models by fine tuning already existing DL models-CNNs, residual neural network (ResNet), visual geometry group (VGG-19), network mobile net V2 based model and classifies satellite images of RSI-CB256 data set in to four classes namely cloudy, desert, green_area and water. For the RSI-CB256 dataset, appropriate network structures are explored in this research to get good performance. The CNN, ResNet and VGG-19 base models achieved an accuracy of 90.48, 92.68 and 91.18 respectively. While the mobile net V2 based model outperformed the other three models with 96.83% accuracy.
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Poornima, E., Suryadevara Mohit, Kunduru Cheresh Reddy, Vallepu Hemchandra, Awadhesh Chandramauli, and Peram Kondal Rao. "Deep Generative Models for Automated Dehazing Remote Sensing Satellite Images." E3S Web of Conferences 430 (2023): 01024. http://dx.doi.org/10.1051/e3sconf/202343001024.
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Remote Sensing (RS) is the process of observing and measuring the physical features of an area from a distance by monitoring its reflected and emitted radiation, usually from a satellite or aircraft. The application of RS spans a wide range of fields, including precision agriculture, disaster management, military operations, environmental monitoring, and weather assessment, among others. Haze or pollution in the satellite images, makes satellite images unsightly and makes valuable information useless. Sometimes satellites must capture images in haze-filled atmospheres, rendering them unusable for study. This proposed work is implemented using the Modern Deep Learning techniques to dehaze the satellite images. We have proposed two GAN architectures, INC-Pix2Pix and RNX-Pix2Pix. A publicly available dataset was used for training our proposed approaches. To eliminate haze from images, we have suggested Deep Generative models by employing the best developments in the field of image processing. By using generative models, images can be dehazed without information loss, supporting the paper’s objective. It has the capacity to learn any kind of underlying data distribution using its learning mechanism. Therefore, it can dehaze satellite images that have been corrupted by haze using the approach automated dehazing remote sensing satellite images using deep learning models . Existing systems can be made more efficient by integrating this approach.
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Zhao, Hang, Yamin Zhang, Qiangqiang Jiang, Xiaofeng Wei, Shizhong Li, and Bo Chen. "Software-Defined Satellite Observation: A Fast Method Based on Virtual Resource Pools." Remote Sensing 15, no. 22 (2023): 5388. http://dx.doi.org/10.3390/rs15225388.
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In recent years, the proliferation of remote sensing satellites has dramatically increased the demands of Earth observation and observing efficiency. Designing a promising satellite resource scheduling method is a pivotal way to meet the requirements of this scenario. However, with hundreds or more satellites involved, the existing optimization methods struggle to address the NP-hard resource scheduling problem effectively. In this paper, an approach named software-defined satellite observation (SDSO) is proposed. First, adopting the new design ideology, we define a unified specification based on a discrete spatial grid to describe the observation capability of all satellites. The observation resources are virtualized using the virtual resource pool technique and then stored in the database in advance, implementing on-demand acquisition for observation resources. Next, we designed a model of the remote sensing satellite resource scheduling problem based on a virtual resource pool and designed a solution method for searching information within the virtual resource pool. Finally, the experimental results show that the computational efficiency of the proposed SDSO methodology has a substantial advantage over the traditional methods. Meanwhile, with the growing number of satellites involved in scheduling, there is only a slight degradation in the execution performance of our method, while the time complexity of optimization-based approaches increases exponentially.
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Bareth, G., and C. Hütt. "UPSCALING AND VALIDATION OF RTK-DIRECT GEOREFERENCED UAV-BASED RGB IMAGE DATA WITH PLANET IMAGERY USING POLYGON GRIDS FOR PASTURE MONITORING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 29, 2021): 533–38. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-533-2021.
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Abstract. The monitoring of managed grasslands with remote sensing methods is becoming more important for spatial decision support. Various remote sensing data acquisition techniques are applied for that purpose in different spatial resolutions ranging from UAV-borne to satellite-based remote sensing. In the last decade, UAV-borne imaging and analysis techniques or in the focus of crop and grassland monitoring and provide very high spatial resolutions. In contrast, satellite data are only available in high to moderate spatial resolutions. In this contribution, we introduce direct georeferenced data acquisition with a Phantom 4 RTK for pasture monitoring and investigate the upscaling of the cm data to satellite resolutions using polygon grids.
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Kabir, Sakib, Larry Leigh, and Dennis Helder. "Vicarious Methodologies to Assess and Improve the Quality of the Optical Remote Sensing Images: A Critical Review." Remote Sensing 12, no. 24 (2020): 4029. http://dx.doi.org/10.3390/rs12244029.
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Over the past decade, number of optical Earth-observing satellites performing remote sensing has increased substantially, dramatically increasing the capability to monitor the Earth. The quantity of remote sensing satellite increase is primarily driven by improved technology, miniaturization of components, reduced manufacturing, and launch cost. These satellites often lack on-board calibrators that a large satellite utilizes to ensure high quality (radiometric, geometric, spatial quality, etc.) scientific measurement. To address this issue, this work presents “best” vicarious image quality assessment and improvement techniques for those kinds of optical satellites which lack an on-board calibration system. In this article, image quality categories have been explored, and essential quality parameters (absolute and relative calibration, aliasing, etc.) have been identified. For each of the parameters, appropriate characterization methods are identified along with their specifications or requirements. In cases of multiple methods, recommendations have been made based-on the strengths and weaknesses of each method. Furthermore, processing steps have been presented, including examples. Essentially, this paper provides a comprehensive study of the criteria that need to be assessed to evaluate remote sensing satellite data quality, and the best vicarious methodologies to evaluate identified quality parameters such as coherent noise and ground sample distance.
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Gerner, Martin, and Marion Pause. "Advancing Learning Assignments in Remote Sensing of the Environment Through Simulation Games." Remote Sensing 12, no. 4 (2020): 735. http://dx.doi.org/10.3390/rs12040735.
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Environmental remote sensing has faced increasing satellite data availability, advanced algorithms for thematic analysis, and novel concepts of ground truth. For that reason, contents and concepts of learning and teaching remote sensing are constantly evolving. This eventually leads to the intuition of methodologically linking academic learning assignments with case-related scopes of application. In order to render case-related learning possible, smart teaching and interactive learning contexts are appreciated and required for remote sensing. That is due to the fact that those contexts are considered promising to trigger and gradually foster students’ comprehensive interdisciplinary thinking. To this end, the following contribution introduces the case-related concept of applying simulation games as a promising didactic format in teaching/learning assignments of remote sensing. As to methodology, participating students have been invited to take on individual roles bound to technology-related profiles (e.g., satellite-mission planning, irrigation, etc.) Based on the scenario, stakeholder teams have been requested to elaborate, analyze and negotiate viable solutions for soil moisture monitoring in a defined context. Collaboration has been encouraged by providing the protected, specifically designed remoSSoil-incubator environment. This letter-type paper aims to introduce the simulation game technique in the context of remote sensing as a type of scholarly teaching; it evaluates learning outcomes by adopting certain techniques of scholarship of teaching and learning (SoTL); and it provides food for thought of replicating, adapting and enhancing simulation games as an innovative, disruptive next-generation learning environment in remote sensing.
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Dahu, Butros M., Khuder Alaboud, Avis Anya Nowbuth, Hunter M. Puckett, Grant J. Scott, and Lincoln R. Sheets. "The Role of Remote Sensing and Geospatial Analysis for Understanding COVID-19 Population Severity: A Systematic Review." International Journal of Environmental Research and Public Health 20, no. 5 (2023): 4298. http://dx.doi.org/10.3390/ijerph20054298.
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Remote sensing (RS), satellite imaging (SI), and geospatial analysis have established themselves as extremely useful and very diverse domains for research associated with space, spatio-temporal components, and geography. We evaluated in this review the existing evidence on the application of those geospatial techniques, tools, and methods in the coronavirus pandemic. We reviewed and retrieved nine research studies that directly used geospatial techniques, remote sensing, or satellite imaging as part of their research analysis. Articles included studies from Europe, Somalia, the USA, Indonesia, Iran, Ecuador, China, and India. Two papers used only satellite imaging data, three papers used remote sensing, three papers used a combination of both satellite imaging and remote sensing. One paper mentioned the use of spatiotemporal data. Many studies used reports from healthcare facilities and geospatial agencies to collect the type of data. The aim of this review was to show the use of remote sensing, satellite imaging, and geospatial data in defining features and relationships that are related to the spread and mortality rate of COVID-19 around the world. This review should ensure that these innovations and technologies are instantly available to assist decision-making and robust scientific research that will improve the population health diseases outcomes around the globe.
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Japitana, M. V., and M. E. C. Burce. "A Satellite-based Remote Sensing Technique for Surface Water Quality Estimation." Engineering, Technology & Applied Science Research 9, no. 2 (2019): 3965–70. http://dx.doi.org/10.48084/etasr.2664.
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Remote sensing provides a synoptic view of the earth surface that can provide spatial and temporal trends necessary for comprehensive water quality (WQ) monitoring and assessment. This study explores the applicability of Landsat 8 and regression analysis in developing models for estimating WQ parameters such as pH, dissolved oxygen (DO), total dissolved solids (TDS), total suspended solids (TSS), biological oxygen demand (BOD), turbidity, and conductivity. The input image was radiometrically-calibrated using fast line-of-sight atmospheric analysis (FLAASH) and then atmospherically corrected to obtain surface reflectance (SR) bands using FLAASH and dark object subtraction (DOS) for comparison. SR bands derived using FLAASH and DOS, water indices, band ratio, and principal component analysis (PCA) images were utilized as input data. Feature vectors were then collected from the input bands and subsequently regressed together with the WQ data. Forward regression results yielded significant high R2 values for all WQ parameters except TSS and conductivity which had only 60.1% and 67.7% respectively. Results also showed that the regression models of pH, BOD, TSS, TDS, DO, and conductivity are highly significant to SR bands derived using DOS. Furthermore, the results of this study showed the promising potential of using RS-based WQ models in performing periodic WQ monitoring and assessment.
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Japitana, M. V., and M. E. C. Burce. "A Satellite-based Remote Sensing Technique for Surface Water Quality Estimation." Engineering, Technology & Applied Science Research 9, no. 2 (2019): 3965–70. https://doi.org/10.5281/zenodo.2647815.
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Remote sensing provides a synoptic view of the earth surface that can provide spatial and temporal trends necessary for comprehensive water quality (WQ) monitoring and assessment. This study explores the applicability of Landsat 8 and regression analysis in developing models for estimating WQ parameters such as pH, dissolved oxygen (DO), total dissolved solids (TDS), total suspended solids (TSS), biological oxygen demand (BOD), turbidity, and conductivity. The input image was radiometrically-calibrated using fast line-of-sight atmospheric analysis (FLAASH) and then atmospherically corrected to obtain surface reflectance (SR) bands using FLAASH and dark object subtraction (DOS) for comparison. SR bands derived using FLAASH and DOS, water indices, band ratio, and principal component analysis (PCA) images were utilized as input data. Feature vectors were then collected from the input bands and subsequently regressed together with the WQ data. Forward regression results yielded significant high R2 values for all WQ parameters except TSS and conductivity which had only 60.1% and 67.7% respectively. Results also showed that the regression models of pH, BOD, TSS, DO, and conductivity are highly significant to SR bands derived using DOS. Furthermore, the results of this study showed the promising potential of using RS-based WQ models in performing periodic WQ monitoring and assessment.
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Pujianiki, Ni Nyoman, and Komang Gede Putra Airlangga. "Analysis of Bathymetry Accuracy Using Sentinel 2 Satellite on Different Characteristics Waters in Bali Island." International Journal on Advanced Science, Engineering and Information Technology 14, no. 4 (2024): 1363–72. http://dx.doi.org/10.18517/ijaseit.14.4.19934.
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Bathymetry surveys today are often carried out using the echo-sounding method, but this method has disadvantages, such as requiring a lot of time and being quite expensive. Along with the development of technology, some alternative methods can be used to visualize bathymetry, such as remote sensing. Remote Sensing uses satellite imagery in the operation, while the technique to acquire bathymetry is called Satellite-Derived Bathymetry (SDB). This method uses an optical satellite with several color bands or multispectral images. In this research, a satellite used to map ocean depth is Sentinel-2. The SDB technique used in this research is the Lyzenga Algorithm. The Lyzenga algorithm uses multilinear logarithms in its operation and can be used using three optical image channels (blue, green, and red channels). Supported by the SDB algorithm, an analysis of research locations was carried out at several points in the waters of Bali Island due to the diversity of water characteristics such as sea depth and wave height. From several analysis results of different characteristic waters in Bali Island, We can see that many parameters impact the result of the Satellite to visualize bathymetry. The Satellite's optimal result for reading the bathymetry depth is approximately 30 meters. But in reality, some cases can interfere with the accuracy of Satellite visualizing bathymetry within this depth. Breaking waves, high water sedimentation, and some objects that could guide the Satellite to misread them as elevation.