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Journal articles on the topic 'Multispectral imagery'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Shi, Weibo, Xiaohan Liao, Jia Sun, et al. "Optimizing Observation Plans for Identifying Faxon Fir (Abies fargesii var. Faxoniana) Using Monthly Unmanned Aerial Vehicle Imagery." Remote Sensing 15, no. 8 (2023): 2205. http://dx.doi.org/10.3390/rs15082205.

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Faxon fir (Abies fargesii var. faxoniana), as a dominant tree species in the subalpine coniferous forest of Southwest China, has strict requirements regarding the temperature and humidity of the growing environment. Therefore, the dynamic and continuous monitoring of Faxon fir distribution is very important to protect this highly sensitive ecological environment. Here, we combined unmanned aerial vehicle (UAV) imagery and convolutional neural networks (CNNs) to identify Faxon fir and explored the identification capabilities of multispectral (five bands) and red-green-blue (RGB) imagery under d

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Edwards, Justin, and Mohamed El-Sharkawy. "FTFNet: Multispectral Image Segmentation." Journal of Low Power Electronics and Applications 13, no. 3 (2023): 42. http://dx.doi.org/10.3390/jlpea13030042.

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Semantic segmentation is a machine learning task that is seeing increased utilization in multiple fields, from medical imagery to land demarcation and autonomous vehicles. A real-time autonomous system must be lightweight while maintaining reasonable accuracy. This research focuses on leveraging the fusion of long-wave infrared (LWIR) imagery with visual spectrum imagery to fill in the inherent performance gaps when using visual imagery alone. This approach culminated in the Fast Thermal Fusion Network (FTFNet), which shows marked improvement over the baseline architecture of the Multispectral

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3

Das, S., and W. K. Krebs. "Sensor fusion of multispectral imagery." Electronics Letters 36, no. 13 (2000): 1115. http://dx.doi.org/10.1049/el:20000839.

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Tang, Hongzhao, Junfeng Xie, Xinming Tang, Wei Chen, and Qi Li. "On-Orbit Radiometric Performance of GF-7 Satellite Multispectral Imagery." Remote Sensing 14, no. 4 (2022): 886. http://dx.doi.org/10.3390/rs14040886.

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China’s first civilian, sub-meter, high-resolution stereo mapping satellite, GF-7, launched on 3 November 2019. Radiometric characterization of GF-7 multispectral imagery has been performed in this study. A relative radiometric accuracy evaluation of the GF-7 multispectral imagery was performed using several large uniform scenes, and the results showed that the accuracy is better than 2%. The absolute radiometric evaluation of the GF-7 satellite sensor was conducted at the Baotou and Dunhuang calibration sites, using the reflectance-based vicarious approach. The synchronous measurements of sur

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Anderson, Connor J., Daniel Heins, Keith C. Pelletier, and Joseph F. Knight. "Using Voting-Based Ensemble Classifiers to Map Invasive Phragmites australis." Remote Sensing 15, no. 14 (2023): 3511. http://dx.doi.org/10.3390/rs15143511.

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Machine learning is frequently combined with imagery acquired from uncrewed aircraft systems (UASs) to detect invasive plants. Having prior knowledge of which machine learning algorithm will produce the most accurate results is difficult. This study examines the efficacy of a voting-based ensemble classifier to identify invasive Phragmites australis from three-band (red, green, blue; RGB) and five-band (red, green, blue, red edge, near-infrared; multispectral; MS) UAS imagery acquired over multiple Minnesota wetlands. A Random Forest, histogram-based gradient-boosting classification tree, and

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Chancia, Robert, Jan van Aardt, Sarah Pethybridge, Daniel Cross, and John Henderson. "Predicting Table Beet Root Yield with Multispectral UAS Imagery." Remote Sensing 13, no. 11 (2021): 2180. http://dx.doi.org/10.3390/rs13112180.

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Timely and accurate monitoring has the potential to streamline crop management, harvest planning, and processing in the growing table beet industry of New York state. We used unmanned aerial system (UAS) combined with a multispectral imager to monitor table beet (Beta vulgaris ssp. vulgaris) canopies in New York during the 2018 and 2019 growing seasons. We assessed the optimal pairing of a reflectance band or vegetation index with canopy area to predict table beet yield components of small sample plots using leave-one-out cross-validation. The most promising models were for table beet root cou

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Sun, Mingyue, Qian Li, Xuzi Jiang, Tiantian Ye, Xinju Li, and Beibei Niu. "Estimation of Soil Salt Content and Organic Matter on Arable Land in the Yellow River Delta by Combining UAV Hyperspectral and Landsat-8 Multispectral Imagery." Sensors 22, no. 11 (2022): 3990. http://dx.doi.org/10.3390/s22113990.

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Rapid and large-scale estimation of soil salt content (SSC) and organic matter (SOM) using multi-source remote sensing is of great significance for the real-time monitoring of arable land quality. In this study, we simultaneously predicted SSC and SOM on arable land in the Yellow River Delta (YRD), based on ground measurement data, unmanned aerial vehicle (UAV) hyperspectral imagery, and Landsat-8 multispectral imagery. The reflectance averaging method was used to resample UAV hyperspectra to simulate the Landsat-8 OLI data (referred to as fitted multispectra). Correlation analyses and the mul

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Xing, Jian, Chaoyong Wang, Ying Liu, et al. "UAV Multispectral Imagery Predicts Dead Fuel Moisture Content." Forests 14, no. 9 (2023): 1724. http://dx.doi.org/10.3390/f14091724.

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Forest floor dead fuel moisture content (DFMC) is an important factor in the occurrence of forest fires, and predicting DFMC is important for accurate fire risk forecasting. Large areas of forest surface DFMC are difficult to predict via manual methods. In this paper, we propose an unmanned aerial vehicle (UAV)-based forest surface DFMC prediction method, in which a UAV is equipped with a multispectral camera to collect multispectral images of dead combustible material on the forest surface over a large area, combined with a deep-learning algorithm to achieve the large-scale prediction of DFMC

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Galbraith, A. E., J. Theiler, K. J. Thome, and R. W. Ziolkowski. "Resolution enhancement of multilook imagery for the multispectral thermal imager." IEEE Transactions on Geoscience and Remote Sensing 43, no. 9 (2005): 1964–77. http://dx.doi.org/10.1109/tgrs.2005.853569.

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Gwon, Yeonghwa, Dongsu Kim, and Siyoon Kwon. "Riverine Bathymetry Survey Using Unmanned Aerial Vehicle-Based Multispectral Image." GEO DATA 5, no. 3 (2023): 195–206. http://dx.doi.org/10.22761/gd.2023.0033.

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River basin surveys are conducted with the aim of providing essential foundational information for the formulation of water management policies, as mandated by relevant laws and regulations. These surveys cover key investigation areas necessary for river basin management, including basic conditions, water conveyance, dimensions, environmental ecology, and more. Among the survey methods, the utilization of remote sensing data, such as drone monitoring imagery and satellite imagery, is employed for various purposes such as the safety management of hydraulic structures like dams and embankments,

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Yang, Bo, Timothy L. Hawthorne, Hannah Torres, and Michael Feinman. "Using Object-Oriented Classification for Coastal Management in the East Central Coast of Florida: A Quantitative Comparison between UAV, Satellite, and Aerial Data." Drones 3, no. 3 (2019): 60. http://dx.doi.org/10.3390/drones3030060.

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High resolution mapping of coastal habitats is invaluable for resource inventory, change detection, and inventory of aquaculture applications. However, coastal areas, especially the interior of mangroves, are often difficult to access. An Unmanned Aerial Vehicle (UAV), equipped with a multispectral sensor, affords an opportunity to improve upon satellite imagery for coastal management because of the very high spatial resolution, multispectral capability, and opportunity to collect real-time observations. Despite the recent and rapid development of UAV mapping applications, few articles have qu

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Gowravaram, Saket, Haiyang Chao, Andrew Molthan, et al. "Spectral Reflectance Estimation of UAS Multispectral Imagery Using Satellite Cross-Calibration Method." Photogrammetric Engineering & Remote Sensing 87, no. 10 (2021): 735–46. http://dx.doi.org/10.14358/pers.20-00091r2.

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This paper introduces a satellite-based cross-calibration (SCC) method for spectral reflectance estimation of unmanned aircraft system (UAS) multispectral imagery. The SCC method provides a low-cost and feasible solution to convert high-resolution UAS images in digital numbers (DN) to reflectance when satellite data is available. The proposed method is evaluated using a multispectral data set, including orthorectified KHawk UAS DN imagery and Landsat 8 Operational Land Imager Level-2 surface reflectance (SR) data over a forest/grassland area. The estimated UAS reflectance images are compared w

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Butko, I. M., O. I. Golubenko, O. M. Makoveichuk, I. O. Zaitsev, and V. O. Kromkach. "Vegetation zone segmentation in multispectral imagery." IOP Conference Series: Earth and Environmental Science 1415, no. 1 (2024): 012068. https://doi.org/10.1088/1755-1315/1415/1/012068.

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Abstract In the paper showing A U-Net-type convolutional neural network proposed for vegetation segmentation in multispectral imagery. The architecture of this network has modified and expanded to achieve better results with a smaller training dataset. The use of the network has made it possible to improve the solution to the problem of accurately identifying objects from the background based on existing information without the use of additional means of obtaining data. The processing of images of a corresponding nature is shown for 4-channel multispectral satellite images using a U-Net-shaped

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Zawada, D. G. "Image processing of underwater multispectral imagery." IEEE Journal of Oceanic Engineering 28, no. 4 (2003): 583–94. http://dx.doi.org/10.1109/joe.2003.819157.

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15

Saghri, J. A., A. G. Tescher, and J. T. Reagan. "Practical transform coding of multispectral imagery." IEEE Signal Processing Magazine 12, no. 1 (1995): 32–43. http://dx.doi.org/10.1109/79.363506.

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16

Katkovsky, Leonid. "Atmospheric correction of multispectral satellite imagery." Ukrainian journal of remote sensing, no. 25 (June 25, 2020): 4–11. http://dx.doi.org/10.36023/ujrs.2020.25.170.

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Atmospheric correction is a necessary step in the processing of remote sensing data acquired in the visible and NIR spectral bands.The paper describes the developed atmospheric correction technique for multispectral satellite data with a small number of relatively broad spectral bands (not hyperspectral). The technique is based on the proposed analytical formulae that expressed the spectrum of outgoing radiation at the top of a cloudless atmosphere with rather high accuracy. The technique uses a model of the atmosphere and its optical and physical parameters that are significant from the point

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Philpot, William D. "Bathymetric mapping with passive multispectral imagery." Applied Optics 28, no. 8 (1989): 1569. http://dx.doi.org/10.1364/ao.28.001569.

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Pagnutti, Mary, Robert E. Ryan, Michelle Kelly, et al. "Radiometric characterization of IKONOS multispectral imagery." Remote Sensing of Environment 88, no. 1-2 (2003): 53–68. http://dx.doi.org/10.1016/j.rse.2003.07.008.

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Tishchenko, A. P., V. V. Asmus, and V. P. Pyatkin. "DIGITAL PROCESSING OF MULTISPECTRAL SPACE IMAGERY." Mapping Sciences and Remote Sensing 22, no. 3 (1985): 173–98. http://dx.doi.org/10.1080/07493878.1985.10641589.

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Rong, ZHANG, LIU Zheng kai, and ZAN Shu. "Wavelet-based Compression for Multispectral Imagery." National Remote Sensing Bulletin, no. 2 (2000): 100–105. http://dx.doi.org/10.11834/jrs.20000204.

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Cheshkova, A. F., and V. S. Riksen. "Strawberry Disease Detection Using Multispectral UAV Imagery." Agricultural Machinery and Technologies 19, no. 2 (2025): 45–52. https://doi.org/10.22314/2073-7599-2025-19-2-45-52.

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Accurate, timely, and non-invasive diagnosis of plant diseases is essential in the industrial cultivation of strawberries, as it helps minimize yield losses and reduce treatment costs. With the advancement of unmanned aerial vehicles and sensor technologies, remote sensing has emerged as a promising tool for monitoring crop health and detecting diseases. Early detection is especially important for sensitive crops such as garden strawberries. (Research purpose) The research aims to evaluate the potential of using multispectral sensors and unmanned aerial vehicles for detecting fungal diseases i

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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 a

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Lazaridou, M. A., and A. Ch Karagianni. "LANDSAT 8 MULTISPECTRAL AND PANSHARPENED IMAGERY PROCESSING ON THE STUDY OF CIVIL ENGINEERING ISSUES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 941–45. http://dx.doi.org/10.5194/isprs-archives-xli-b8-941-2016.

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Scientific and professional interests of civil engineering mainly include structures, hydraulics, geotechnical engineering, environment, and transportation issues. Topics included in the context of the above may concern urban environment issues, urban planning, hydrological modelling, study of hazards and road construction. Land cover information contributes significantly on the study of the above subjects. Land cover information can be acquired effectively by visual image interpretation of satellite imagery or after applying enhancement routines and also by imagery classification. The Landsat

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Lazaridou, M. A., and A. Ch Karagianni. "LANDSAT 8 MULTISPECTRAL AND PANSHARPENED IMAGERY PROCESSING ON THE STUDY OF CIVIL ENGINEERING ISSUES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 941–45. http://dx.doi.org/10.5194/isprsarchives-xli-b8-941-2016.

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Scientific and professional interests of civil engineering mainly include structures, hydraulics, geotechnical engineering, environment, and transportation issues. Topics included in the context of the above may concern urban environment issues, urban planning, hydrological modelling, study of hazards and road construction. Land cover information contributes significantly on the study of the above subjects. Land cover information can be acquired effectively by visual image interpretation of satellite imagery or after applying enhancement routines and also by imagery classification. The Landsat

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Cheng, Xiaohui, Bingwu Li, Yun Deng, Jian Tang, Yuanyuan Shi, and Junyu Zhao. "MMDL-Net: Multi-Band Multi-Label Remote Sensing Image Classification Model." Applied Sciences 14, no. 6 (2024): 2226. http://dx.doi.org/10.3390/app14062226.

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High-resolution remote sensing imagery comprises spatial structure features of multispectral bands varying in scale, color, and shape. These heterogeneous geographical features introduce grave challenges to the fine segmentation required for classification applications in remote sensing imagery, where direct application of traditional image classification models fails to deliver optimal results. To overcome these challenges, a multispectral, multi-label model, MMDL-Net, has been developed. This model is integrated with the multi-label BigEarthNet dataset, primarily employed for land cover clas

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Huo, Langning, Iryna Matsiakh, Jonas Bohlin, and Michelle Cleary. "Estimation of Tree Vitality Reduced by Pine Needle Disease Using Multispectral Drone Images." Remote Sensing 17, no. 2 (2025): 271. https://doi.org/10.3390/rs17020271.

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Multispectral imagery from unmanned aerial vehicles (UAVs) can provide high-resolution data to map tree mortality caused by pests or diseases. Although many studies have investigated UAV-imagery-based methods to detect trees under acute stress followed by tree mortality, few have tested the feasibility and accuracy of detecting trees under chronic stress. This study aims to develop methods and test how well UAV-based multispectral imagery can detect pine needle disease long before tree mortality. Multispectral images were acquired four times through the growing season in an area with pine tree

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Tan, Hongjian, Weili Kou, Weiheng Xu, Leiguang Wang, Huan Wang, and Ning Lu. "Improved Estimation of Aboveground Biomass in Rubber Plantations Using Deep Learning on UAV Multispectral Imagery." Drones 9, no. 1 (2025): 32. https://doi.org/10.3390/drones9010032.

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The accurate estimation of aboveground biomass (AGB) in rubber plantations is essential for predicting rubber production and assessing carbon storage. Multispectral sensors mounted on unmanned aerial vehicles (UAVs) can obtain high spatiotemporal resolution imagery of rubber plantations, offering significant advantages in capturing fine structural details and heterogeneity. However, most previous studies primarily focused on developing biomass estimation models for rubber using machine learning (ML) algorithms in conjunction with feature selection methods based on UAV-acquired multispectral im

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Mochalov, V. F. "Assessment of vegetation state based on modified fuzzy algorithm." Informatization and communication 5 (December 2020): 169–74. http://dx.doi.org/10.34219/2078-8320-2020-11-5-169-174.

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Assessment of forest vegetation is based on multispectral space imagery processing. The issues of the formation of initial data and selection of test sites for automated identification of landscape elements are considered. The aim of the work is to present an assessment technology for natural objects that applies the mathematical apparatus of fuzzy logic to automated processing of multispectral aerospace imagery. An example of assessing the forest state and determining the consequences of a forest fi re is presented.

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Boon, M. A., and S. Tesfamichael. "WETLAND VEGETATION INTEGRITY ASSESSMENT WITH LOW ALTITUDE MULTISPECTRAL UAV IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W6 (August 23, 2017): 55–62. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w6-55-2017.

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The use of multispectral sensors on Unmanned Aerial Vehicles (UAVs) was until recently too heavy and bulky although this changed in recent times and they are now commercially available. The focus on the usage of these sensors is mostly directed towards the agricultural sector where the focus is on precision farming. Applications of these sensors for mapping of wetland ecosystems are rare. Here, we evaluate the performance of low altitude multispectral UAV imagery to determine the state of wetland vegetation in a localised spatial area. Specifically, NDVI derived from multispectral UAV imagery

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Pope, A., T. A. Scambos, M. Moussavi, et al. "Estimating supraglacial lake depth in West Greenland using Landsat 8 and comparison with other multispectral methods." Cryosphere 10, no. 1 (2016): 15–27. http://dx.doi.org/10.5194/tc-10-15-2016.

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Abstract. Liquid water stored on the surface of ice sheets and glaciers impacts surface mass balance, ice dynamics, and heat transport. Multispectral remote sensing can be used to detect supraglacial lakes and estimate their depth and area. In this study, we use in situ spectral and bathymetric data to assess lake depth retrieval using the recently launched Landsat 8 Operational Land Imager (OLI). We also extend our analysis to other multispectral sensors to evaluate their performance with similar methods. Digital elevation models derived from WorldView stereo imagery (pre-lake filling and pos

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Piccoli, Flavio, Mirko Paolo Barbato, Marco Peracchi, and Paolo Napoletano. "Estimation of Soil Characteristics from Multispectral Sentinel-3 Imagery and DEM Derivatives Using Machine Learning." Sensors 23, no. 18 (2023): 7876. http://dx.doi.org/10.3390/s23187876.

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In this paper, different machine learning methodologies have been evaluated for the estimation of the multiple soil characteristics of a continental-wide area corresponding to the European region, using multispectral Sentinel-3 satellite imagery and digital elevation model (DEM) derivatives. The results confirm the importance of multispectral imagery in the estimation of soil properties and specifically show that the use of DEM derivatives improves the quality of the estimates, in terms of R2, by about 19% on average. In particular, the estimation of soil texture increases by about 43%, and th

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Jiang, Xiaohua, Xiaoxiao Zhang, Ming Liu, and Jie Tian. "Joint Panchromatic and Multispectral Geometric Calibration Method for the DS-1 Satellite." Remote Sensing 16, no. 2 (2024): 433. http://dx.doi.org/10.3390/rs16020433.

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The DS-1 satellite was launched successfully on 3 June 2021 from the Taiyuan Satellite Launch Center. The satellite is equipped with a 1 m panchromatic and a 4 m multispectral sensor, providing high-resolution and wide-field optical remote sensing imaging capabilities. For satellites equipped with panchromatic and multispectral sensors, conventional geometric processing methods in the past involved separate calibration for the panchromatic sensor and the multispectral sensor. This method produced distinct internal and external calibration parameters in the respective bands, and also resulted i

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Sangeetha, A. "Damaged Building Detection from Satellite Multispectral Imagery." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 3367–73. http://dx.doi.org/10.22214/ijraset.2021.35706.

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Damaged Building footprint detection in satellite and aerial imagery is crucial in city management. Building detection is a fundamental but a challenging problem mainly because it requires correct recovery of building footprints from high-resolution images. Buildings are one of the key pieces of cadastral information related to population and cities, and are fundamental to urban planning & policymaking. Critical infrastructures, such as public transport, electricity, water distribution networks, or postal and delivery services, rely heavily on accurate population and building maps. On top

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Wang, J., M. R. Azimi-Sadjadi, and D. Reinke. "A Temporally Adaptive Classifier for Multispectral Imagery." IEEE Transactions on Neural Networks 15, no. 1 (2004): 159–65. http://dx.doi.org/10.1109/tnn.2003.820622.

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Toet, Alexander, Michael J. de Jong, Maarten A. Hogervorst, and Ignace T. C. Hooge. "Perceptual evaluation of color transformed multispectral imagery." Optical Engineering 53, no. 4 (2014): 043101. http://dx.doi.org/10.1117/1.oe.53.4.043101.

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Alparone, Luciano. "Estimating noise and information of multispectral imagery." Optical Engineering 41, no. 3 (2002): 656. http://dx.doi.org/10.1117/1.1447547.

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Tescher, Andrew G. "Class-prioritized compression of multispectral imagery data." Journal of Electronic Imaging 11, no. 2 (2002): 246. http://dx.doi.org/10.1117/1.1455008.

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Csatho, Bea M., Cornelis J. Van Der Veen, and Catherine M. Tremper. "Trimline Mapping from Multispectral Landsat ETM+ Imagery." Géographie physique et Quaternaire 59, no. 1 (2006): 49–62. http://dx.doi.org/10.7202/013736ar.

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AbstractMultispectral Landsat ETM+ imagery is used to study the ice-marginal region in the vicinity of Jakobshavn Isfjord, west Greenland. In particular, the trimline indicating margin retreat since the maximum stand attained during the Little Ice Age maximum is reconstructed, and compared with earlier maps based on aerial photogrammetry and ground surveys. Applying supervised classification, fourteen different surface types were identified, ranging from snow and ice, debris-covered ice and water with differing turbidities, to different types of vegetative landcover. After similar classes were

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Bittner, J. L., M. T. Schill, L. M. Blaha, and J. W. Houpt. "Ideal Observer Analysis of Fused Multispectral Imagery." Journal of Vision 14, no. 10 (2014): 1359. http://dx.doi.org/10.1167/14.10.1359.

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Jun, Li, and Zhou Fengxian. "Computer identification of multispectral satellite cloud imagery." Advances in Atmospheric Sciences 7, no. 3 (1990): 366–75. http://dx.doi.org/10.1007/bf03179768.

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Septyadi, Dedi, Enrico Gracia, and Riza Putera Syamsuddin. "TEA PLANTATION MAPPING USING UAV MULTISPECTRAL IMAGERY." International Journal of Remote Sensing and Earth Sciences (IJReSES) 20, no. 1 (2023): 08. http://dx.doi.org/10.30536/j.ijreses.2023.v20.a3817.

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Tea is one of Indonesia’s most famous commodities, which is dominantly planted on the Java Island of Indonesia. Tea is one of the leading sources of exports, and the Indonesian government is very concerned about the stability of their export commodity sustainability. Therefore, monitoring and evaluating its sustainability and availability become necessary. One of the solutions to the tea plantation monitoring and management program is mapping through remote sensing and GIS. In this study, high-resolution multispectral imageries are captured from a UAV and used to map the tea plantation with th

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Zhang, Yanchao, Wen Yang, Ying Sun, Christine Chang, Jiya Yu, and Wenbo Zhang. "Fusion of Multispectral Aerial Imagery and Vegetation Indices for Machine Learning-Based Ground Classification." Remote Sensing 13, no. 8 (2021): 1411. http://dx.doi.org/10.3390/rs13081411.

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Unmanned Aerial Vehicles (UAVs) are emerging and promising platforms for carrying different types of cameras for remote sensing. The application of multispectral vegetation indices for ground cover classification has been widely adopted and has proved its reliability. However, the fusion of spectral bands and vegetation indices for machine learning-based land surface investigation has hardly been studied. In this paper, we studied the fusion of spectral bands information from UAV multispectral images and derived vegetation indices for almond plantation classification using several machine lear

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Prior, Elizabeth M., Frances C. O’Donnell, Christian Brodbeck, Wesley N. Donald, George Brett Runion, and Stephanie L. Shepherd. "Measuring High Levels of Total Suspended Solids and Turbidity Using Small Unoccupied Aerial Systems (sUAS) Multispectral Imagery." Drones 4, no. 3 (2020): 54. http://dx.doi.org/10.3390/drones4030054.

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Due to land development, high concentrations of suspended sediment are produced from erosion after rain events. Sediment basins are commonly used for the settlement of suspended sediments before discharge. Stormwater regulations may require frequent sampling and monitoring of these basins, both of which are time and labor intensive. Potential remedies are small, unoccupied aerial systems (sUAS). The goal of this study was to demonstrate whether sUAS multispectral imagery could measure high levels of total suspended solids (TSS) and turbidity in a sediment basin. The sediment basin at the Aubur

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Soni, Ayush, Alexander Loui, Scott Brown, and Carl Salvaggio. "High-quality multispectral image generation using Conditional GANs." Electronic Imaging 2020, no. 8 (2020): 86–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.8.imawm-086.

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In this paper, we demonstrate the use of a Conditional Generative Adversarial Networks (cGAN) framework for producing high-fidelity, multispectral aerial imagery using low-fidelity imagery of the same kind as input. The motivation behind is that it is easier, faster, and often less costly to produce low-fidelity images than high-fidelity images using the various available techniques, such as physics-driven synthetic image generation models. Once the cGAN network is trained and tuned in a supervised manner on a data set of paired low- and high-quality aerial images, it can then be used to enhan

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Pope, A., T. A. Scambos, M. Moussavi, et al. "Estimating supraglacial lake depth in western Greenland using Landsat 8 and comparison with other multispectral methods." Cryosphere Discussions 9, no. 3 (2015): 3257–92. http://dx.doi.org/10.5194/tcd-9-3257-2015.

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Abstract. Liquid water stored on the surface of ice sheets and glaciers impacts surface mass balance, ice dynamics, and heat transport. Supraglacial lakes, therefore, play a significant role in ice sheet behavior as a surface expression of the glacial hydrological system. Multispectral remote sensing can be used to detect supraglacial lakes and estimate their depth and area. In this study, we use in situ spectral and bathymetric data to assess lake depth retrieval using the recently launched Landsat 8 Operational Land Imager (OLI). We also extend our analysis to other multispectral sensors to

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Bird, Clara N., Allison H. Dawn, Julian Dale, and David W. Johnston. "A Semi-Automated Method for Estimating Adélie Penguin Colony Abundance from a Fusion of Multispectral and Thermal Imagery Collected with Unoccupied Aircraft Systems." Remote Sensing 12, no. 22 (2020): 3692. http://dx.doi.org/10.3390/rs12223692.

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Monitoring Adélie penguin (Pygoscelis adeliae) populations on the Western Antarctic Peninsula (WAP) provides information about the health of the species and the WAP marine ecosystem itself. In January 2017, surveys of Adélie penguin colonies at Avian Island and Torgersen Island off the WAP were conducted via unoccupied aircraft systems (UAS) collecting optical Red Green Blue (RGB), thermal, and multispectral imagery. A semi-automated workflow to count individual penguins using a fusion of multispectral and thermal imagery was developed and combined into an ArcGIS workflow. This workflow isolat

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Su, Lihong, and Yuxia Huang. "Seagrass Resource Assessment Using WorldView-2 Imagery in the Redfish Bay, Texas." Journal of Marine Science and Engineering 7, no. 4 (2019): 98. http://dx.doi.org/10.3390/jmse7040098.

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Seagrass meadows play important roles as habitats for many marine organisms, traps for sediment, and buffers against wave actions. The objective of this paper is to map seagrass meadows in the Redfish Bay, Texas from WorldView-2 imagery. Seagrass meadows grow in shallow and clear water areas in the Redfish Bay. The WorldView-2 satellite can acquire multispectral imagery from the bay bottom with 2 m spatial resolution 8 multispectral bands and 0.46 m panchromatic imagery. The top of atmosphere radiance was transformed to the bottom reflectance through the atmospheric correction and the water co

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Matejčíková, Júlia, Dana Vébrová, and Peter Surový. "Comparative Analysis of Machine Learning Techniques and Data Sources for Dead Tree Detection: What Is the Best Way to Go?" Remote Sensing 16, no. 16 (2024): 3086. http://dx.doi.org/10.3390/rs16163086.

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In Central Europe, the extent of bark beetle infestation in spruce stands due to prolonged high temperatures and drought has created large areas of dead trees, which are difficult to monitor by ground surveys. Remote sensing is the only possibility for the assessment of the extent of the dead tree areas. Several options exist for mapping individual dead trees, including different sources and different processing techniques. Satellite images, aerial images, and images from UAVs can be used as sources. Machine and deep learning techniques are included in the processing techniques, although model

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Song, Bonggeun, and Kyunghun Park. "Detection of Aquatic Plants Using Multispectral UAV Imagery and Vegetation Index." Remote Sensing 12, no. 3 (2020): 387. http://dx.doi.org/10.3390/rs12030387.

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In this study, aquatic plants in a small reservoir were detected using multispectral UAV (Unmanned Aerial Vehicle) imagery and various vegetation indices. A Firefly UAV, which has both fixed-wing and rotary-wing flight modes, was flown over the study site four times. A RedEdge camera was mounted on the UAV to acquire multispectral images. These images were used to analyze the NDVI (Normalized Difference Vegetation Index), ENDVI (Enhance Normalized Difference Vegetation Index), NDREI (Normalized Difference RedEdge Index), NGRDI (Normalized Green-Red Difference Index), and GNDVI (Green Normalize

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Xu, Xinyu, Xiaojun Li, Yikun Li, Lu Kang, and Junfei Ge. "A Novel Adaptively Optimized PCNN Model for Hyperspectral Image Sharpening." Remote Sensing 15, no. 17 (2023): 4205. http://dx.doi.org/10.3390/rs15174205.

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Hyperspectral satellite imagery has developed rapidly over the last decade because of its high spectral resolution and strong material recognition capability. Nonetheless, the spatial resolution of available hyperspectral imagery is inferior, severely affecting the accuracy of ground object identification. In the paper, we propose an adaptively optimized pulse-coupled neural network (PCNN) model to sharpen the spatial resolution of the hyperspectral imagery to the scale of the multispectral imagery. Firstly, a SAM-CC strategy is designed to assign hyperspectral bands to the multispectral bands

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