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Journal articles on the topic 'Remote sensing Remote sensing Multispectral photography Image processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Liebel, L., and M. Körner. "SINGLE-IMAGE SUPER RESOLUTION FOR MULTISPECTRAL REMOTE SENSING DATA USING CONVOLUTIONAL NEURAL NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 883–90. http://dx.doi.org/10.5194/isprs-archives-xli-b3-883-2016.

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In optical remote sensing, spatial resolution of images is crucial for numerous applications. Space-borne systems are most likely to be affected by a lack of spatial resolution, due to their natural disadvantage of a large distance between the sensor and the sensed object. Thus, methods for <i>single-image super resolution</i> are desirable to exceed the limits of the sensor. Apart from assisting visual inspection of datasets, post-processing operations—e.g., segmentation or feature extraction—can benefit from detailed and distinguishable structures. In this paper, we show that rec

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Liebel, L., and M. Körner. "SINGLE-IMAGE SUPER RESOLUTION FOR MULTISPECTRAL REMOTE SENSING DATA USING CONVOLUTIONAL NEURAL NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 883–90. http://dx.doi.org/10.5194/isprsarchives-xli-b3-883-2016.

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In optical remote sensing, spatial resolution of images is crucial for numerous applications. Space-borne systems are most likely to be affected by a lack of spatial resolution, due to their natural disadvantage of a large distance between the sensor and the sensed object. Thus, methods for &lt;i&gt;single-image super resolution&lt;/i&gt; are desirable to exceed the limits of the sensor. Apart from assisting visual inspection of datasets, post-processing operations—e.g., segmentation or feature extraction—can benefit from detailed and distinguishable structures. In this paper,

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Rachkovskaya, E. I., S. S. Temirbekov, and R. E. Sadvokasov. "Application of remote sensing methods for assessment of anthropogenic transformation of rangelands." Geobotanical mapping, no. 1998-2000 (2000): 16–25. http://dx.doi.org/10.31111/geobotmap/1998-2000.16.

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Capabilities of the remote sensing methods for making maps of actual and potential vegetation, and assessment of the extent of anthropogenic transformation of rangelands are presented in the paper. Study area is a large intermountain depression, which is under intensive agricultural use. Color photographs have been made by Aircraft camera Wild Heerburg RC-30 and multispectral scanner Daedalus (AMS) digital aerial data (6 bands, 3.5m resolution) have been used for analysis of distribution and assessment of the state of vegetation. Digital data were processed using specialized program ENVI 3.0.

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Müller, M. U., N. Ekhtiari, R. M. Almeida, and C. Rieke. "SUPER-RESOLUTION OF MULTISPECTRAL SATELLITE IMAGES USING CONVOLUTIONAL NEURAL NETWORKS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-1-2020 (August 3, 2020): 33–40. http://dx.doi.org/10.5194/isprs-annals-v-1-2020-33-2020.

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Abstract. Super-resolution aims at increasing image resolution by algorithmic means and has progressed over the recent years due to advances in the fields of computer vision and deep learning. Convolutional Neural Networks based on a variety of architectures have been applied to the problem, e.g. autoencoders and residual networks. While most research focuses on the processing of photographs consisting only of RGB color channels, little work can be found concentrating on multi-band, analytic satellite imagery. Satellite images often include a panchromatic band, which has higher spatial resolut

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Siok, Katarzyna, Ireneusz Ewiak, and Agnieszka Jenerowicz. "Multi-Sensor Fusion: A Simulation Approach to Pansharpening Aerial and Satellite Images." Sensors 20, no. 24 (2020): 7100. http://dx.doi.org/10.3390/s20247100.

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The growing demand for high-quality imaging data and the current technological limitations of imaging sensors require the development of techniques that combine data from different platforms in order to obtain comprehensive products for detailed studies of the environment. To meet the needs of modern remote sensing, the authors present an innovative methodology of combining multispectral aerial and satellite imagery. The methodology is based on the simulation of a new spectral band with a high spatial resolution which, when used in the pansharpening process, yields an enhanced image with a hig

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Yakubu, Bashir Ishaku, Shua’ib Musa Hassan, and Sallau Osisiemo Asiribo. "AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES." Geosfera Indonesia 3, no. 2 (2018): 27. http://dx.doi.org/10.19184/geosi.v3i2.7934.

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Rapid urbanization rates impact significantly on the nature of Land Cover patterns of the environment, which has been evident in the depletion of vegetal reserves and in general modifying the human climatic systems (Henderson, et al., 2017; Kumar, Masago, Mishra, & Fukushi, 2018; Luo and Lau, 2017). This study explores remote sensing classification technique and other auxiliary data to determine LULCC for a period of 50 years (1967-2016). The LULCC types identified were quantitatively evaluated using the change detection approach from results of maximum likelihood classification algorithm

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Medvedev, Andrey, Arseny Kudikov, Natalia Telnova, Olga Tutubalina, Elena Golubeva, and Mikhail Zimin. "Multiscale assessment of northern forest characteristics based on ultra-high resolution data." Abstracts of the ICA 1 (July 15, 2019): 1. http://dx.doi.org/10.5194/ica-abs-1-246-2019.

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<p><strong>Abstract.</strong> The algorithms for quantitative estimates of various structural and functional parameters of forest ecosystems, particularly boreal forests, on high resolution remote sensing data are actively developing since the mid-2000s. For monitoring of forest ecosystems located at the Northern limit of distribution, effective not only lidar data but also the optical data obtained by unmanned aerial vehicles (UAV’s) with ultra-low altitude photography and derived products resulting from modern algorithms for the photogrammetric processing.</p><p&gt

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SHANKAR, B. UMA, SAROJ K. MEHER, and ASHISH GHOSH. "NEURO-WAVELET CLASSIFIER FOR MULTISPECTRAL REMOTE SENSING IMAGES." International Journal of Wavelets, Multiresolution and Information Processing 05, no. 04 (2007): 589–611. http://dx.doi.org/10.1142/s0219691307001914.

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A neuro-wavelet supervised classifier is proposed for land cover classification of multispectral remote sensing images. Features extracted from the original pixels information using wavelet transform (WT) are fed as input to a feed forward multi-layer neural network (MLP). The WT basically provides the spatial and spectral features of a pixel along with its neighbors and these features are used for improved classification. For testing the performance of the proposed method, we have used two IRS-1A satellite images and one SPOT satellite image. Results are compared with those of the original sp

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Zhao, Yu, Fan Feng Meng, and Jiang Feng. "Unmanned Aerial Vehicle Based Agricultural Remote Sensing Multispectral Image Processing Methods." Advanced Materials Research 905 (April 2014): 585–88. http://dx.doi.org/10.4028/www.scientific.net/amr.905.585.

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In order to provide more flexibility in remote sensing image collection, unmanned aerial vehicle has been used to kinds of agricultural productions. Images acquired from the UAV based RS system were very useful as a result of their high spatial resolution and low turn-around time. This paper discussed general methods to process the multispectral RS data at image process level. The distortion correction caused by sensor was introduced. The geometric distortion comprised sensor distortion and external distortion caused by external parameters. At last, the general image mosaic methods were discus

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Watson, Kenneth. "Introduction to the Special Issue on remote sensing." GEOPHYSICS 52, no. 7 (1987): 839–40. http://dx.doi.org/10.1190/1.1442355.

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In 1977, the first Special Issue on remote sensing published by Geophysics contained papers selected from two special sessions at the 45th Annual International SEG Meeting, October 12–16, 1975, in Denver, Colorado. That first Special Issue consisted of eight papers: four are primarily tutorial (image processing, spectral signatures in the visible and near infrared, microwave spectra of layered media, and factor analysis of gamma‐ray spectrometry), two involve structural interpretations with implications for mineral exploration and seismicity, and two examine multispectral reflectance data for

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Thyrsted, T. "Remote sensing - a new tool in exploration geology." Rapport Grønlands Geologiske Undersøgelse 128 (December 31, 1986): 135–46. http://dx.doi.org/10.34194/rapggu.v128.7930.

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Remote sensing techniques have been applied to mineral exploration in areas of South and East Greenland. The data consist of airborne and satellite-borne (Landsat) multispectral scanner images and geochemical and geophysical measurements interpolated into grid format and registered on the Landsat images. The main image processing methods applied include ratioing, principal component transformation/factor analysis and classification. In addition, visual and subsequent statistical analyses of lineaments were carried out on images from South Greenland. The results of the work include mapping of s

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Zhang, Zhenxing, Feng Gao, Bin Ma, and Zhiqiang Zhang. "Extraction of Earth Surface Texture Features from Multispectral Remote Sensing Data." Journal of Electrical and Computer Engineering 2018 (October 25, 2018): 1–9. http://dx.doi.org/10.1155/2018/9684629.

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Earth surface texture features referring to as visual features of homogeneity in remote sensing images are very important to understand the relationship between surface information and surrounding environment. Remote sensing data contain rich information of earth surface texture features (image gray reflecting the spatial distribution information of texture features, for instance). Here, we propose an efficient and accurate approach to extract earth surface texture features from remote sensing data, called gray level difference frequency spatial (GLDFS). The gray level difference frequency spa

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Laliberte, Andrea S., Mark A. Goforth, Caitriana M. Steele, and Albert Rango. "Multispectral Remote Sensing from Unmanned Aircraft: Image Processing Workflows and Applications for Rangeland Environments." Remote Sensing 3, no. 11 (2011): 2529–51. http://dx.doi.org/10.3390/rs3112529.

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Vasvári, Gyula, Róbert Vig, and Attila Dobos. "Opportunities of delineating inland waters and soil moisture with remote sensed data." Acta Agraria Debreceniensis, no. 46 (May 16, 2012): 95–98. http://dx.doi.org/10.34101/actaagrar/46/2416.

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The methodology for delineating water bodies on multispectral remote sensing imagery was examined and evaluated. A supervised approach is tested with the aim to accurately detect inland water, moisturised soil surface and swampy patches on the Landsat TM 7 scene. The goal of this research is to investigate whether the application of remote sensing image interpretation could further refine the possibilities of future soil conductivity measurement research. The methodologies used were the application of supervised classification algorithms based on the training data collected in the area. The ac

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Chen, Rong. "Application of UAV-Low Altitude Remote Sensing System in Sea Area Supervision." Earth Sciences Research Journal 25, no. 1 (2021): 65–68. http://dx.doi.org/10.15446/esrj.v25n1.94162.

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The sea area supervision is the premise and guarantee of safeguarding national security, protecting national sovereignty, and realizing the development of marine resources, and its importance is self-evident. To carry out the national sea area work more efficiently, this study designed low altitude-Unmanned Aerial Vehicles (UAV) remote sensing system applied to the sea area supervision and analyzed the remote sensing photography technology and remote sensing image processing technology. Experiments verified the effectiveness of the system. The research results show that the UAV-based low altit

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Figueiredo, Marco A., Clay S. Gloster, Mark Stephens, Corey A. Graves, and Mouna Nakkar. "Implementation of Multispectral Image Classification on a Remote Adaptive Computer." VLSI Design 10, no. 3 (2000): 307–19. http://dx.doi.org/10.1155/2000/31983.

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As the demand for higher performance computers for the processing of remote sensing science algorithms increases, the need to investigate new computing paradigms is justified. Field Programmable Gate Arrays enable the implementation of algorithms at the hardware gate level, leading to orders of magnitude performance increase over microprocessor based systems. The automatic classification of spaceborne multispectral images is an example of a computation intensive application that can benefit from implementation on an FPGA-based custom computing machine (adaptive or reconfigurable computer). A p

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Xue, L., C. Liu, Y. Wu, and H. Li. "SEMANTIC SEGMENTATION OF CONVOLUTIONAL NEURAL NETWORK FOR SUPERVISED CLASSIFICATION OF MULTISPECTRAL REMOTE SENSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 2035–39. http://dx.doi.org/10.5194/isprs-archives-xlii-3-2035-2018.

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Semantic segmentation is a fundamental research in remote sensing image processing. Because of the complex maritime environment, the classification of roads, vegetation, buildings and water from remote Sensing Imagery is a challenging task. Although the neural network has achieved excellent performance in semantic segmentation in the last years, there are a few of works using CNN for ground object segmentation and the results could be further improved. This paper used convolution neural network named U-Net, its structure has a contracting path and an expansive path to get high resolution outpu

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Soares, A. R., T. S. Körting, L. M. G. Fonseca, and A. K. Neves. "AN UNSUPERVISED SEGMENTATION METHOD FOR REMOTE SENSING IMAGERY BASED ON CONDITIONAL RANDOM FIELDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W12-2020 (November 4, 2020): 91–95. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-91-2020.

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Abstract. Segmentation is a fundamental problem in image processing and a common operation in Remote Sensing, which has been widely used especially in Geographic Object-Based Image Analysis (GEOBIA). In this paper, we propose a new unsupervised segmentation algorithm based on the Conditional Random Fields (CRF) theory. The method relies on two levels of information: (1) that comes from an unsupervised classification with Fuzzy C-Means algorithm; (2) the 8-connected neighbourhood of a pixel. The algorithm was tested on a WorldView-2 multispectral image, with 2 m of spatial resolution. Results w

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Hnatushenko, V. V., and V. V. Vasyliev. "REMOTE SENSING IMAGE FUSION USING ICA AND OPTIMIZED WAVELET TRANSFORM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 653–59. http://dx.doi.org/10.5194/isprsarchives-xli-b7-653-2016.

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In remote-sensing image processing, fusion (pan-sharpening) is a process of merging high-resolution panchromatic and lower resolution multispectral (MS) imagery to create a single high-resolution color image. Many methods exist to produce data fusion results with the best possible spatial and spectral characteristics, and a number have been commercially implemented. However, the pan-sharpening image produced by these methods gets the high color distortion of spectral information. In this paper, to minimize the spectral distortion we propose a remote sensing image fusion method which combines t

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Hnatushenko, V. V., and V. V. Vasyliev. "REMOTE SENSING IMAGE FUSION USING ICA AND OPTIMIZED WAVELET TRANSFORM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 653–59. http://dx.doi.org/10.5194/isprs-archives-xli-b7-653-2016.

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In remote-sensing image processing, fusion (pan-sharpening) is a process of merging high-resolution panchromatic and lower resolution multispectral (MS) imagery to create a single high-resolution color image. Many methods exist to produce data fusion results with the best possible spatial and spectral characteristics, and a number have been commercially implemented. However, the pan-sharpening image produced by these methods gets the high color distortion of spectral information. In this paper, to minimize the spectral distortion we propose a remote sensing image fusion method which combines t

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Pour, A. B., M. Hashim, and J. K. Hong. "APPLICATION OF MULTISPECTRAL SATELLITE DATA FOR GEOLOGICAL MAPPING IN ANTARCTIC ENVIRONMENTS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W1 (September 29, 2016): 77–81. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w1-77-2016.

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Remote sensing imagery is capable to provide a solution to overcome the difficulties associated with geological field mapping in the Antarctic. Advanced optical and radar satellite imagery is the most applicable tool for mapping and identification of inaccessible regions in Antarctic. Consequently, an improved scientific research using remote sensing technology would be essential to provide new and more complete lithological and structural data to fill the numerous knowledge gaps on Antarctica’s geology. In this investigation, Oscar coast area in Graham Land, Antarctic Peninsula (AP) was selec

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Yang, Wenkao, Jing Wang, and Jing Guo. "A Novel Algorithm for Satellite Images Fusion Based on Compressed Sensing and PCA." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/708985.

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This paper studies the image fusion of high-resolution panchromatic image and low-resolution multispectral image. Based on the classic fusion algorithms on remote sensing image fusion, the PCA (principal component analysis) transform, and discrete wavelet transform, we carry out in-depth research. The compressed sensing (CS) abandons the full sample and shifts the sampling of the signal to sampling information that greatly reduces the potential consumption of traditional signal acquisition and processing. We combine compressed sensing with satellite remote sensing image fusion algorithm and pr

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El-Mimouni, Mohamed, Abdellatif Aarab, Abdellah Lakhloufi, Abderrazak Hamzaoui, Ahmed Akhssas, and Kawtar Benyas. "Contribution of multispectral remote sensing to mining exploration in the Rehamna Massif, Moroccan Meseta." E3S Web of Conferences 150 (2020): 03018. http://dx.doi.org/10.1051/e3sconf/202015003018.

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The Western Moroccan Meseta contains mining sites in operation for several decades and others in development. The Rehamna Massif belonging to this, is the subject of this study. The present study reveals new results on the mineralization in this massif. It is based on the synergy of field investigation data and ASTER image analysis (L1T) covering this massif. Through spectral processing, namely the calculation of the band ratios, ACP and MNF, applied to the nine VNIR and SWIR bands of this image, it was possible to reveal the distribution of hydrothermal alteration minerals in the study area.

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Huo, Lian-Zhi, and Ping Tang. "A graph-based active learning method for classification of remote sensing images." International Journal of Wavelets, Multiresolution and Information Processing 16, no. 04 (2018): 1850023. http://dx.doi.org/10.1142/s0219691318500236.

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Remote sensing (RS) technology provides essential data for monitoring the Earth. To fully utilize the data, image classification is often needed to convert data to information. The success of image classification methods greatly depends on the quality and quantity of training samples. To effectively select more informative training samples, this paper proposes a new active learning (AL) technique for classification of remote sensing (RS) images based on graph theory. A new diversity criterion is proposed based on geometrical features of the support vector machines (SVM) outputs. The diversity

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Cucho-Padin, Gonzalo, Hildo Loayza, Susan Palacios, Mario Balcazar, Mariella Carbajal, and Roberto Quiroz. "Development of low-cost remote sensing tools and methods for supporting smallholder agriculture." Applied Geomatics 12, no. 3 (2019): 247–63. http://dx.doi.org/10.1007/s12518-019-00292-5.

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AbstractAgricultural UAV-based remote sensing tools to facilitate decision-making for increasing productivity in developing countries were developed and tested. Specifically, a high-quality multispectral sensor and sophisticated-yet-user-friendly data processing techniques (software) under an open-access policy were implemented. The multispectral sensor—IMAGRI-CIP—is a low-cost adaptable multi-sensor array that allows acquiring high-quality and low-SNR images from a UAV platform used to estimate vegetation indexes such as NDVI. Also, a set of software tools that included wavelet-based image al

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Ma, Tao, Jie Ma, and Kun Yu. "A Local Feature Descriptor Based on Oriented Structure Maps with Guided Filtering for Multispectral Remote Sensing Image Matching." Remote Sensing 11, no. 8 (2019): 951. http://dx.doi.org/10.3390/rs11080951.

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Multispectral image matching plays a very important role in remote sensing image processing and can be applied for registering the complementary information captured by different sensors. Due to the nonlinear intensity difference in multispectral images, many classic descriptors designed for images of the same spectrum are unable to work well. To cope with this problem, this paper proposes a new local feature descriptor termed histogram of oriented structure maps (HOSM) for multispectral image matching tasks. This proposed method consists of three steps. First, we propose a new method based on

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Han, Hongyin, Chengshan Han, Liang Huang, Taiji Lan, and Xucheng Xue. "Irradiance Restoration Based Shadow Compensation Approach for High Resolution Multispectral Satellite Remote Sensing Images." Sensors 20, no. 21 (2020): 6053. http://dx.doi.org/10.3390/s20216053.

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Numerous applications are hindered by shadows in high resolution satellite remote sensing images, like image classification, target recognition and change detection. In order to improve remote sensing image utilization, significant importance appears for restoring surface feature information under shadow regions. Problems inevitably occur for current shadow compensation methods in processing high resolution multispectral satellite remote sensing images, such as color distortion of compensated shadow and interference of non-shadow. In this study, to further settle these problems, we analyzed th

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Ma, Tian-Hui, Zongben Xu, and Deyu Meng. "Remote Sensing Image Denoising via Low-Rank Tensor Approximation and Robust Noise Modeling." Remote Sensing 12, no. 8 (2020): 1278. http://dx.doi.org/10.3390/rs12081278.

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Noise removal is a fundamental problem in remote sensing image processing. Most existing methods, however, have not yet attained sufficient robustness in practice, due to more or less neglecting the intrinsic structures of remote sensing images and/or underestimating the complexity of realistic noise. In this paper, we propose a new remote sensing image denoising method by integrating intrinsic image characterization and robust noise modeling. Specifically, we use low-Tucker-rank tensor approximation to capture the global multi-factor correlation within the underlying image, and adopt a non-id

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Mohammed, Ali Abdul Wahhab, and Hussein Thary Khamees. "Categorizing and measurement satellite image processing of fire in the forest greece using remote sensing." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (2021): 846. http://dx.doi.org/10.11591/ijeecs.v21.i2.pp846-853.

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This paper has been utilized satellite Sentinel-2A imagery, this satellite is a polar-orbiting, multispectral high-resolution to cover Athens city, Greece that located at latitude (37° 58′ 46″) N, (23° 42′ 58″) E.,the work aims to measurement and study the wildfires natural resourcesbefore and after fire break out that happenedin forests of Athens city in Greece for a year (2007, 2018) and analysis the damage caused by these wildfiresand their impact on environment and soil by categorize the satellite images for the interested region before and after wildfires for a year (2007) and a year (201

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Anurogo, Wenang, Muhammad Zainuddin Lubis, and Mir'atul Khusna Mufida. "Modified Soil-Adjusted Vegetation Index In Multispectral Remote Sensing Data for Estimating Tree Canopy Cover Density at Rubber Plantation." Journal of Geoscience, Engineering, Environment, and Technology 3, no. 1 (2018): 15. http://dx.doi.org/10.24273/jgeet.2018.3.01.1003.

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Forest inventories such as tree canopy density information require a long time and high costs, especially on extensive forest coverage. Remote sensing technology that directly captures the surface vegetation character with extensive recording coverage can be used as an alternative to carrying out such inventory activities. This research aims to determine the level of vegetation canopy cover density on rubber plants that became the location of the research and know the accuracy of the resulting data. The method used in this research is a combination of remote sensing image interpretation, geogr

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Wulder, Mike. "Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters." Progress in Physical Geography: Earth and Environment 22, no. 4 (1998): 449–76. http://dx.doi.org/10.1177/030913339802200402.

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Forests are the most widely distributed ecosystem on the earth, affecting the lives of most humans daily, either as an economic good or an environmental regulator. As forests are a complex and widely distributed ecosystem, remote sensing provides a valuable means of monitoring them. Remote-sensing instruments allow for the collection of digital data through a range of scales in a synoptic and timely manner. Accordingly, a variety of image-processing techniques have been developed for the estimation of forest inventory and biophysical parameters from remotely sensed images. The use of remotely

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Abdolahpoor, Asma, and Peyman Kabiri. "New texture-based pansharpening method using wavelet packet transform and PCA." International Journal of Wavelets, Multiresolution and Information Processing 18, no. 04 (2020): 2050025. http://dx.doi.org/10.1142/s0219691320500253.

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Image fusion is an important concept in remote sensing. Earth observation satellites provide both high-resolution panchromatic and low-resolution multispectral images. Pansharpening is aimed on fusion of a low-resolution multispectral image with a high-resolution panchromatic image. Because of this fusion, a multispectral image with high spatial and spectral resolution is generated. This paper reports a new method to improve spatial resolution of the final multispectral image. The reported work proposes an image fusion method using wavelet packet transform (WPT) and principal component analysi

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Radočaj, Dorijan, Jasmina Obhođaš, Mladen Jurišić, and Mateo Gašparović. "Global Open Data Remote Sensing Satellite Missions for Land Monitoring and Conservation: A Review." Land 9, no. 11 (2020): 402. http://dx.doi.org/10.3390/land9110402.

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The application of global open data remote sensing satellite missions in land monitoring and conservation studies is in the state of rapid growth, ensuring an observation with high spatial and spectral resolution over large areas. The purpose of this study was to provide a review of the most important global open data remote sensing satellite missions, current state-of-the-art processing methods and applications in land monitoring and conservation studies. Multispectral (Landsat, Sentinel-2, and MODIS), radar (Sentinel-1), and digital elevation model missions (SRTM, ASTER) were analyzed, as th

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Rajendran, Sankaran, and Sobhi Nasir. "Recognition of Minerals Using Multispectral Remote Sensing Data: A Case Study in the Sultanate of Oman." Sultan Qaboos University Journal for Science [SQUJS] 19, no. 2 (2015): 37. http://dx.doi.org/10.24200/squjs.vol19iss2pp37-52.

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The present study demonstrates the capability of a multispectral sensor for the detection of the minerals in the rocks surrounding the Rusayl and Al Jafnayn regions, Sultanate of Oman. The study of spectral absorptions of rocks and minerals in the visible and near infrared (VNIR) and short wavelength infrared (SWIR) spectral bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) using the Spectral Angle Mapper (SAM) supervised image classification technique has provided information on the occurrence of minerals in the rock types of the regions. The study shows the

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Žížala, Daniel, Robert Minařík, and Tereza Zádorová. "Soil Organic Carbon Mapping Using Multispectral Remote Sensing Data: Prediction Ability of Data with Different Spatial and Spectral Resolutions." Remote Sensing 11, no. 24 (2019): 2947. http://dx.doi.org/10.3390/rs11242947.

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The image spectral data, particularly hyperspectral data, has been proven as an efficient data source for mapping of the spatial variability of soil organic carbon (SOC). Multispectral satellite data are readily available and cost-effective sources of spectral data compared to costly and technically demanding processing of hyperspectral data. Moreover, their continuous acquisition allows to develop a composite from time-series, increasing the spatial coverage of SOC maps. In this study, an evaluation of the prediction ability of models assessing SOC using real multispectral remote sensing data

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Beiranvand Pour, A., M. Hashim, and M. Pournamdari. "CHROMITITE PROSPECTING USING LANDSAT TM AND ASTER REMOTE SENSING DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-2/W2 (October 19, 2015): 99–103. http://dx.doi.org/10.5194/isprsannals-ii-2-w2-99-2015.

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Studying the ophiolite complexes using multispectral remote sensing satellite data are interesting because of high diversity of minerals and the source of podiform chromitites. This research developed an approach to discriminate lithological units and detecting host rock of chromitite bodies within ophiolitic complexes using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat Thematic Mapper (TM) satellite data. Three main ophiolite complexes located in south of Iran have been selected for the study. Spectral transform techniques, including minimum noise frac

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Anifadi, A., Is Parcharidis, and O. Sykioti. "HYDROTHERMAL ALTERATION ZONES DETECTION IN LIMNOS ISLAND, THROUGH THE APPLICATION OF REMOTE SENSING." Bulletin of the Geological Society of Greece 50, no. 3 (2017): 1596. http://dx.doi.org/10.12681/bgsg.11879.

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In this study we use Landsat 8 OLI satellite imagery in order to identify and map alteration zones in Limnos island (N. Aegean, Greece). Pre-processing included sea and vegetation masking. In order to enhance spatial resolution, data fusion to 15m is performed. A lineament map is extracted from the panchromatic image that gives the general tectonic view of the island. The detection and mapping of alteration minerals is performed using specific band ratios and consequent composite images. The colour composite using bands 10, 11, 7 (RGB) show the spectral signature and general distribution of si

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Xiong, Quan, Yuan Wang, Diyou Liu, et al. "A Cloud Detection Approach Based on Hybrid Multispectral Features with Dynamic Thresholds for GF-1 Remote Sensing Images." Remote Sensing 12, no. 3 (2020): 450. http://dx.doi.org/10.3390/rs12030450.

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Nowadays, GF-1 (GF is the acronym for GaoFen which means high-resolution in Chinese) remote sensing images are widely utilized in agriculture because of their high spatio-temporal resolution and free availability. However, due to the transferrable rationale of optical satellites, the GF-1 remote sensing images are inevitably impacted by clouds, which leads to a lack of ground object’s information of crop areas and adds noises to research datasets. Therefore, it is crucial to efficiently detect the cloud pixel of GF-1 imagery of crop areas with powerful performance both in time consumption and

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Trinh, Le Hung, and V. R. Zabloskii. "The Method of Detection of Clay Minerals and Iron Oxide Based on Landsat Multispectral Images (as Exemplified in the Territory of Thai Nguyen Province, Vietnam)." Mining science and technology 4, no. 1 (2019): 65–75. http://dx.doi.org/10.17073/2500-0632-2019-1-65-75.

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Landsat multispectral images have been successfully used for discovering some mineral deposits in different regions of the world. Some minerals, including clay minerals and iron oxide, can be detected by multispectral surveys due to their spectral characteristics. This paper presents the results of the application of principal component analysis and Crosta technique for detecting accumulations of clay minerals and iron oxide based on a Landsat 8 Oli multispectral image of Thai Nguyen Province, north of Vietnam. The obtained results have demonstrated the feasibility and suitability of prompt de

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Yang, Qi Wei, Bo Jin, and Jing Xiong Huang. "Aircraft Navigation Model Based on Genetic Algorithms." Advanced Materials Research 834-836 (October 2013): 1377–81. http://dx.doi.org/10.4028/www.scientific.net/amr.834-836.1377.

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Integrating spectrum and navigation technology is an impotant research field. Using air-ground position model for earth observation is a hot topic in the field of photogrammetry and remote-sensing. When in the absence of ground control points or only a small amount of ground control points, through the acquisition and display of images, data processing and analysis of a series of process, accurate navigation information for aircraft can be provided. In this paper a new method of multispectral navigationOtsu method is presented. Begin with the images of spectral features, the paper explored the

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López, Josué, Deni Torres, Stewart Santos, and Clement Atzberger. "Spectral Imagery Tensor Decomposition for Semantic Segmentation of Remote Sensing Data through Fully Convolutional Networks." Remote Sensing 12, no. 3 (2020): 517. http://dx.doi.org/10.3390/rs12030517.

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This work aims at addressing two issues simultaneously: data compression at input space and semantic segmentation. Semantic segmentation of remotely sensed multi- or hyperspectral images through deep learning (DL) artificial neural networks (ANN) delivers as output the corresponding matrix of pixels classified elementwise, achieving competitive performance metrics. With technological progress, current remote sensing (RS) sensors have more spectral bands and higher spatial resolution than before, which means a greater number of pixels in the same area. Nevertheless, the more spectral bands and

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Stepchenko, Arthur. "LAND COVER CLASSIFICATION BASED ON MODIS IMAGERY DATA USING ARTIFICIAL NEURAL NETWORKS." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 2 (June 15, 2017): 159. http://dx.doi.org/10.17770/etr2017vol2.2545.

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Remote sensing has been widely used to obtain land cover information using automated classification. Land cover is a measure of what is overlaying the surface of the earth. Accurate mapping of land cover on a regional scale is useful in such fields as precision agriculture or forest management and is one of the most important applications in remote sensing. In this study, multispectral MODIS Terra NDVI images and an artificial neural network (ANN) were used in land cover classification. Artificial neural network is a computing tool that is designed to simulate the way the human brain analyzes

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Song, Shiran, Jianhua Liu, Heng Pu, Yuan Liu, and Jingyan Luo. "The Comparison of Fusion Methods for HSRRSI Considering the Effectiveness of Land Cover (Features) Object Recognition Based on Deep Learning." Remote Sensing 11, no. 12 (2019): 1435. http://dx.doi.org/10.3390/rs11121435.

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The efficient and accurate application of deep learning in the remote sensing field largely depends on the pre-processing technology of remote sensing images. Particularly, image fusion is the essential way to achieve the complementarity of the panchromatic band and multispectral bands in high spatial resolution remote sensing images. In this paper, we not only pay attention to the visual effect of fused images, but also focus on the subsequent application effectiveness of information extraction and feature recognition based on fused images. Based on the WorldView-3 images of Tongzhou District

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Cao, Feng, Fei Liu, Han Guo, Wenwen Kong, Chu Zhang, and Yong He. "Fast Detection of Sclerotinia Sclerotiorum on Oilseed Rape Leaves Using Low-Altitude Remote Sensing Technology." Sensors 18, no. 12 (2018): 4464. http://dx.doi.org/10.3390/s18124464.

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Sclerotinia sclerotiorum, one of the major diseases infecting oilseed rape leaves, has seriously affected crop yield and quality. In this study, an indoor unmanned aerial vehicle (UAV) low-altitude remote sensing simulation platform was built for disease detection. Thermal, multispectral and RGB images were acquired before and after being artificially inoculated with Sclerotinia sclerotiorum on oilseed rape leaves. New image registration and fusion methods based on scale-invariant feature transform (SIFT) were presented to construct a fused database using multi-model images. The changes of tem

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Li, Jin, Fei Xing, Ting Sun, and Zheng You. "Multispectral Image Compression Based on DSC Combined with CCSDS-IDC." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/738735.

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Remote sensing multispectral image compression encoder requires low complexity, high robust, and high performance because it usually works on the satellite where the resources, such as power, memory, and processing capacity, are limited. For multispectral images, the compression algorithms based on 3D transform (like 3D DWT, 3D DCT) are too complex to be implemented in space mission. In this paper, we proposed a compression algorithm based on distributed source coding (DSC) combined with image data compression (IDC) approach recommended by CCSDS for multispectral images, which has low complexi

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Chrétien, L. P., J. Théau, and P. Ménard. "WILDLIFE MULTISPECIES REMOTE SENSING USING VISIBLE AND THERMAL INFRARED IMAGERY ACQUIRED FROM AN UNMANNED AERIAL VEHICLE (UAV)." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1/W4 (August 26, 2015): 241–48. http://dx.doi.org/10.5194/isprsarchives-xl-1-w4-241-2015.

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Wildlife aerial surveys require time and significant resources. Multispecies detection could reduce costs to a single census for species that coexist spatially. Traditional methods are demanding for observers in terms of concentration and are not adapted to multispecies censuses. The processing of multispectral aerial imagery acquired from an unmanned aerial vehicle (UAV) represents a potential solution for multispecies detection. The method used in this study is based on a multicriteria object-based image analysis applied on visible and thermal infrared imagery acquired from a UAV. This proje

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Golilarz, Noorbakhsh Amiri, Hui Gao, Saied Pirasteh, Mohammad Yazdi, Junlin Zhou, and Yan Fu. "Satellite Multispectral and Hyperspectral Image De-Noising with Enhanced Adaptive Generalized Gaussian Distribution Threshold in the Wavelet Domain." Remote Sensing 13, no. 1 (2020): 101. http://dx.doi.org/10.3390/rs13010101.

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The presence of noise in remote sensing satellite images may cause limitations in analysis and object recognition. Noise suppression based on thresholding neural network (TNN) and optimization algorithms perform well in de-noising. However, there are some problems that need to be addressed. Furthermore, finding the optimal threshold value is a challenging task for learning algorithms. Moreover, in an optimization-based noise removal technique, we must utilize the optimization algorithm to overcome the problem. These methods are effective at reducing noise but may blur some parts of an image, a

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Mikula, Karol, Mária Šibíková, Martin Ambroz, et al. "NaturaSat—A Software Tool for Identification, Monitoring and Evaluation of Habitats by Remote Sensing Techniques." Remote Sensing 13, no. 17 (2021): 3381. http://dx.doi.org/10.3390/rs13173381.

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The NaturaSat software integrates various image processing techniques together with vegetation data, into one multipurpose tool that is designed for performing facilities for all requirements of habitat exploration, all in one place. It provides direct access to multispectral Sentinel-2 data provided by the European Space Agency. It supports using these data with various vegetation databases, in a user-friendly environment, for, e.g., vegetation scientists, fieldwork experts, and nature conservationists. The presented study introduces the NaturaSat software, describes new powerful tools, such

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Peng, X. Y., G. Q. Zhou, H. B. Yan, T. Yue, J. Liu, and Y. X. Mu. "BLOCK ORTHORECTIFICATION AND MOSAIC OF 1960s DISP IMAGES IN QUJING, YUNNAN." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 635–39. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-635-2020.

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Abstract. Remote sensing digital image mosaic refers to the splicing of two or more remote sensing images into a panoramic image to meet the application requirements of wide images. It is becoming more and more important to cover large areas of mosaic images. The data pre-processing and mosaic process are different for different images. In this paper, the declassified intelligence satellite photography (DISP) in the 1960s was used as experimental data, taking Qujing in Yunnan Province as an example, a total of 92 scene images were used for DISP image processing and seamless mosaic. Firstly, In

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Illarionova, Svetlana, Dmitrii Shadrin, Alexey Trekin, Vladimir Ignatiev, and Ivan Oseledets. "Generation of the NIR Spectral Band for Satellite Images with Convolutional Neural Networks." Sensors 21, no. 16 (2021): 5646. http://dx.doi.org/10.3390/s21165646.

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The near-infrared (NIR) spectral range (from 780 to 2500 nm) of the multispectral remote sensing imagery provides vital information for landcover classification, especially concerning vegetation assessment. Despite the usefulness of NIR, it does not always accomplish common RGB. Modern achievements in image processing via deep neural networks make it possible to generate artificial spectral information, for example, to solve the image colorization problem. In this research, we aim to investigate whether this approach can produce not only visually similar images but also an artificial spectral

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