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Artículos de revistas sobre el tema "High spatial and spectral remote sensing"

Autor: Grafiati

Publicado: 11 de marzo de 2023

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1

Rocchini, Duccio. "Ecological Remote Sensing: A Challenging Section on Ecological Theory and Remote Sensing." Remote Sensing 13, no. 5 (2021): 848. http://dx.doi.org/10.3390/rs13050848.

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2

Han, Yanling, Cong Wei, Ruyan Zhou, Zhonghua Hong, Yun Zhang, and Shuhu Yang. "Combining 3D-CNN and Squeeze-and-Excitation Networks for Remote Sensing Sea Ice Image Classification." Mathematical Problems in Engineering 2020 (April 7, 2020): 1–15. http://dx.doi.org/10.1155/2020/8065396.

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Sea ice is one of the most prominent marine disasters in high latitudes. Remote sensing technology provides an effective means for sea ice detection. Remote sensing sea ice images contain rich spectral and spatial information. However, most traditional methods only focus on spectral information or spatial information, and do not excavate the feature of spectral and spatial simultaneously in remote sensing sea ice images classification. At the same time, the complex correlation characteristics among spectra and small sample problem in sea ice classification also limit the improvement of sea ice

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3

Wei, Lifei, Ming Yu, Yajing Liang, et al. "Precise Crop Classification Using Spectral-Spatial-Location Fusion Based on Conditional Random Fields for UAV-Borne Hyperspectral Remote Sensing Imagery." Remote Sensing 11, no. 17 (2019): 2011. http://dx.doi.org/10.3390/rs11172011.

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The precise classification of crop types is an important basis of agricultural monitoring and crop protection. With the rapid development of unmanned aerial vehicle (UAV) technology, UAV-borne hyperspectral remote sensing imagery with high spatial resolution has become the ideal data source for the precise classification of crops. For precise classification of crops with a wide variety of classes and varied spectra, the traditional spectral-based classification method has difficulty in mining large-scale spatial information and maintaining the detailed features of the classes. Therefore, a pre

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4

Duan, Meimei, and Lijuan Duan. "High Spatial Resolution Remote Sensing Data Classification Method Based on Spectrum Sharing." Scientific Programming 2021 (December 20, 2021): 1–12. http://dx.doi.org/10.1155/2021/4356957.

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Existing remote sensing data classification methods cannot achieve the sharing of remote sensing image spectrum, leading to poor fusion and classification of remote sensing data. Therefore, a high spatial resolution remote sensing data classification method based on spectrum sharing is proposed. A page frame recovery algorithm (PFRA) is introduced to allocate the wireless spectrum resources in low-frequency band, and a dynamic spectrum sharing mechanism is designed between the primary and secondary users of remote sensing images. Based on this, D-S evidence theory is used to fuse high spatial

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5

Peng, Mingyuan, Lifu Zhang, Xuejian Sun, Yi Cen, and Xiaoyang Zhao. "A Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset." Remote Sensing 12, no. 23 (2020): 3888. http://dx.doi.org/10.3390/rs12233888.

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With the growing development of remote sensors, huge volumes of remote sensing data are being utilized in related applications, bringing new challenges to the efficiency and capability of processing huge datasets. Spatiotemporal remote sensing data fusion can restore high spatial and high temporal resolution remote sensing data from multiple remote sensing datasets. However, the current methods require long computing times and are of low efficiency, especially the newly proposed deep learning-based methods. Here, we propose a fast three-dimensional convolutional neural network-based spatiotemp

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6

Imanian, A., M. H. Tangestani, and A. Asadi. "INVESTIGATION OF SPECTRAL CHARACTERISTICS OF CARBONATE ROCKS – A CASE STUDY ON POSHT MOLEH MOUNT IN IRAN." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 553–57. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-553-2019.

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Abstract. Recent developments in the image processing approaches and the availability of multi and/or hyper spectral remote sensing data with high spectral, spatial and temporal resolutions have made remote sensing technique of great interest in investigations of geological sciences. One of the biggest advantage of the application of remote sensing in geology is recognizing the type of unknown rocks and minerals. In this study, an investigation on spectral features of carbonate rocks (i.e. calcite, dolomite, and dolomitized calcite) were done in terms of main absorptions, the reasons of those

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7

Xu, Qingsong, Xin Yuan, Chaojun Ouyang, and Yue Zeng. "Attention-Based Pyramid Network for Segmentation and Classification of High-Resolution and Hyperspectral Remote Sensing Images." Remote Sensing 12, no. 21 (2020): 3501. http://dx.doi.org/10.3390/rs12213501.

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Unlike conventional natural (RGB) images, the inherent large scale and complex structures of remote sensing images pose major challenges such as spatial object distribution diversity and spectral information extraction when existing models are directly applied for image classification. In this study, we develop an attention-based pyramid network for segmentation and classification of remote sensing datasets. Attention mechanisms are used to develop the following modules: (i) a novel and robust attention-based multi-scale fusion method effectively fuses useful spatial or spectral information at

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8

NanLan, Wang, and Zeng Xiaoyong. "Hyperspectral Data Classification Algorithm considering Spatial Texture Features." Mobile Information Systems 2022 (March 22, 2022): 1–11. http://dx.doi.org/10.1155/2022/9915809.

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As a cutting-edge technology, hyperspectral remote sensing has been widely applied in many fields, including agricultural production, mineral identification, target detection, disaster warning, military reconnaissance, and urban planning. The collected hyperspectral data have high spectral resolution and spatial resolution and are characterized by a large amount of information, redundancy, and high dimension. At the same time, there is a strong correlation between the bands. Therefore, hyperspectral data not only provides rich information but also brings great challenges for subsequent process

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9

Zhao, Rui, and Shihong Du. "Spectral-Spatial Residual Network for Fusing Hyperspectral and Panchromatic Remote Sensing Images." Remote Sensing 14, no. 3 (2022): 800. http://dx.doi.org/10.3390/rs14030800.

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Fusing hyperspectral and panchromatic remote sensing images can obtain the images with high resolution in both spectral and spatial domains. In addition, it can complement the deficiency of high-resolution hyperspectral and panchromatic remote sensing images. In this paper, a spectral–spatial residual network (SSRN) model is established for the intelligent fusion of hyperspectral and panchromatic remote sensing images. Firstly, the spectral–spatial deep feature branches are built to extract the representative spectral and spatial deep features, respectively. Secondly, an enhanced multi-scale r

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10

Shi, Xue, Yu Wang, Yu Li, and Shiqing Dou. "Remote Sensing Image Segmentation Based on Hierarchical Student’s-t Mixture Model and Spatial Constrains with Adaptive Smoothing." Remote Sensing 15, no. 3 (2023): 828. http://dx.doi.org/10.3390/rs15030828.

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Image segmentation is an important task in image processing and analysis but due to the same ground object having different spectra and different ground objects having similar spectra, segmentation, particularly on high-resolution remote sensing images, can be significantly challenging. Since the spectral distribution of high-resolution remote sensing images can have complex characteristics (e.g., asymmetric or heavy-tailed), an innovative image segmentation algorithm is proposed based on the hierarchical Student’s-t mixture model (HSMM) and spatial constraints with adaptive smoothing. Conside

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11

Zhou, Xiao Hu. "Geometric Distortion Correction of Geothermal Field Hyperspectral Remote Sensing Images in Lintong, Shanxi." Advanced Materials Research 383-390 (November 2011): 4158–62. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.4158.

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The accurate delineation of geothermal resources with remote sensing technology is one hot topic in recent years. Remote sensing images used in previous studies, are mostly space multi-spectral remote sensing images, spectral resolution and spatial resolution are relatively low, difficult to accurately delineate the geothermal anomaly. Considering those research at home and abroad, using Hyperspectral resolution remote sensing images, choosing Lintong area for the study area. Because of hyperspectral remote sensing images exist in the acquisition process more obvious geometric distortion, duri

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12

Ge, Chuting, Haiyong Ding, Inigo Molina, Yongjian He, and Daifeng Peng. "Object-Oriented Change Detection Method Based on Spectral–Spatial–Saliency Change Information and Fuzzy Integral Decision Fusion for HR Remote Sensing Images." Remote Sensing 14, no. 14 (2022): 3297. http://dx.doi.org/10.3390/rs14143297.

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Spectral features in remote sensing images are extensively utilized to detect land cover changes. However, detection noise appearing in the changing maps due to the abundant spatial details in the high-resolution images makes it difficult to acquire an accurate interpretation result. In this paper, an object-oriented change detection approach is proposed which integrates spectral–spatial–saliency change information and fuzzy integral decision fusion for high-resolution remote sensing images with the purpose of eliminating the impact of detection noise. First, to reduce the influence of feature

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13

Harlander, John M., Fred L. Roesler, Christoph R. Englert, Joel G. Cardon, and Jeff Wimperis. "Spatial Heterodyne Spectroscopy For High Spectral Resolution Space-Based Remote Sensing." Optics and Photonics News 15, no. 1 (2004): 46. http://dx.doi.org/10.1364/opn.15.1.000046.

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14

Zhang, Shuang, Yifei Han, Hua Wang, and Daishuang Hou. "Gram-Schmidt Remote Sensing Image Fusion Algorithm Based on Matrix Elementary Transformation." Journal of Physics: Conference Series 2410, no. 1 (2022): 012013. http://dx.doi.org/10.1088/1742-6596/2410/1/012013.

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Abstract In the application of the Internet of vehicles, it is difficult for a single remote sensing image to have accurate spatial information and spectral information at the same time, so engineers must use image fusion to improve the utilization of remote sensing image information. Aiming at the shortcomings of low spectral resolution and relatively complex programming of traditional fusion algorithms, this paper proposes a Gram-Schmidt remote sensing image fusion algorithm based on matrix elementary transformation, which aims to improve the spatial resolution of multispectral images by usi

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15

Marang, Ian J., Patrick Filippi, Tim B. Weaver, et al. "Machine Learning Optimised Hyperspectral Remote Sensing Retrieves Cotton Nitrogen Status." Remote Sensing 13, no. 8 (2021): 1428. http://dx.doi.org/10.3390/rs13081428.

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Hyperspectral imaging spectrometers mounted on unmanned aerial vehicle (UAV) can capture high spatial and spectral resolution to provide cotton crop nitrogen status for precision agriculture. The aim of this research was to explore machine learning use with hyperspectral datacubes over agricultural fields. Hyperspectral imagery was collected over a mature cotton crop, which had high spatial (~5.2 cm) and spectral (5 nm) resolution over the spectral range 475–925 nm that allowed discrimination of individual crop rows and field features as well as a continuous spectral range for calculating deri

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16

Cui, B., W. J. Huang, H. C. Ye, Q. X. Chen, Z. C. Li, and H. Y. Jiang. "Optimal spatial resolution of remote-sensing imagery for monitoring cantaloupe greenhouses." IOP Conference Series: Earth and Environmental Science 1004, no. 1 (2022): 012020. http://dx.doi.org/10.1088/1755-1315/1004/1/012020.

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Abstract Plastic greenhouses are vital agricultural facilities to protect cash crops from disease and insects, especially in the Hainan region of China, which has high temperature and high humidity. Remote-sensing technology is an efficient means to quickly determine the spatial distribution of plastic greenhouses on the regional scale. With the rapid development of remote-sensing technology, and especially the increasing types of high-spatial-resolution remote-sensing imagery, many studies have obtained good results by using remote-sensing technology to monitor plastic greenhouses. However, t

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17

Guan, X., W. Qi, J. He, Q. Wen, T. Chen, and Z. Wang. "PURIFICATION OF TRAINING SAMPLES BASED ON SPECTRAL FEATURE AND SUPERPIXEL SEGMENTATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 425–30. http://dx.doi.org/10.5194/isprs-archives-xlii-3-425-2018.

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Remote sensing image classification is an effective way to extract information from large volumes of high-spatial resolution remote sensing images. Generally, supervised image classification relies on abundant and high-precision training data, which is often manually interpreted by human experts to provide ground truth for training and evaluating the performance of the classifier. Remote sensing enterprises accumulated lots of manually interpreted products from early lower-spatial resolution remote sensing images by executing their routine research and business programs. However, these manuall

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18

Sun, Y., Y. Lin, X. Hu, et al. "THE STUDY OF SPECTRUM RECONSTRUCTION BASED ON FUZZY SET FULL CONSTRAINT AND MULTIENDMEMBER DECOMPOSITION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 551–55. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-551-2017.

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Hyperspectral imaging system can obtain spectral and spatial information simultaneously with bandwidth to the level of 10&amp;thinsp;nm or even less. Therefore, hyperspectral remote sensing has the ability to detect some kinds of objects which can not be detected in wide-band remote sensing, making it becoming one of the hottest spots in remote sensing. In this study, under conditions with a fuzzy set of full constraints, Normalized Multi-Endmember Decomposition Method (NMEDM) for vegetation, water, and soil was proposed to reconstruct hyperspectral data using a large number of high-qualit

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19

Kumar, Suresh, and Vijay Bhagat. "Remote Sensing Satellites for Land Applications: A Review." Remote Sensing of Land 2, no. 2 (2019): 96–104. http://dx.doi.org/10.21523/gcj1.18020203.

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Satellite remote sensing offers a unique opportunity in deriving various components of land information by integrating with ground based observation. Currently several remote sensing satellites are providing multispectral, hyperspectral and microwave data to cater the need of various land applications. Several old age remote sensing satellites have been updated with new generation satellites offering high spatial, spectral and temporal resolution. Microwave remote sensing data is now available with high spatial resolution and providing land information in cloudy weather condition that strength

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20

Shao, Donghang, Wenbo Xu, Hongyi Li, Jian Wang, and Xiaohua Hao. "Modeling Snow Surface Spectral Reflectance in a Land Surface Model Targeting Satellite Remote Sensing Observations." Remote Sensing 12, no. 18 (2020): 3101. http://dx.doi.org/10.3390/rs12183101.

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Snow surface spectral reflectance is very important in the Earth’s climate system. Traditional land surface models with parameterized schemes can simulate broadband snow surface albedo but cannot accurately simulate snow surface spectral reflectance with continuous and fine spectral wavebands, which constitute the major observations of current satellite sensors; consequently, there is an obvious gap between land surface model simulations and remote sensing observations. Here, we suggest a new integrated scheme that couples a radiative transfer model with a land surface model to simulate high s

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21

Wu, Yuanyuan, Siling Feng, Cong Lin, Haijie Zhou, and Mengxing Huang. "A Three Stages Detail Injection Network for Remote Sensing Images Pansharpening." Remote Sensing 14, no. 5 (2022): 1077. http://dx.doi.org/10.3390/rs14051077.

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Multispectral (MS) pansharpening is crucial to improve the spatial resolution of MS images. MS pansharpening has the potential to provide images with high spatial and spectral resolutions. Pansharpening technique based on deep learning is a topical issue to deal with the distortion of spatio-spectral information. To improve the preservation of spatio-spectral information, we propose a novel three-stage detail injection pansharpening network (TDPNet) for remote sensing images. First, we put forward a dual-branch multiscale feature extraction block, which extracts four scale details of panchroma

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22

Bai, Shi, and Jie Zhao. "A New Strategy to Fuse Remote Sensing Data and Geochemical Data with Different Machine Learning Methods." Remote Sensing 15, no. 4 (2023): 930. http://dx.doi.org/10.3390/rs15040930.

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Geochemical data can reflect geological features, making it one of the basic types of geodata that have been widely used in mineral exploration, environmental assessment, resource potential analysis and other research. However, final decisions regarding activities are often limited by the spatial accuracy of geochemical data. Geochemical sampling is sometimes difficult to conduct because of harsh natural and geographic conditions (e.g., mountainous areas with high altitude and complex terrain), meaning that only medium/low-precision survey data could be obtained, which may not be adequate for

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23

Pena, J. A., T. Yumin, H. Liu, B. Zhao, J. A. Garcia, and J. Pinto. "REMOTE SENSING DATA FUSION TO DETECT ILLICIT CROPS AND UNAUTHORIZED AIRSTRIPS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1363–68. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1363-2018.

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Remote sensing data fusion has been playing a more and more important role in crop planting area monitoring, especially for crop area information acquisition. Multi-temporal data and multi-spectral time series are two major aspects for improving crop identification accuracy. Remote sensing fusion provides high quality multi-spectral and panchromatic images in terms of spectral and spatial information, respectively. In this paper, we take one step further and prove the application of remote sensing data fusion in detecting illicit crop through LSMM, GOBIA, and MCE analyzing of strategic informa

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24

Tasdemir, Kadim, Yaser Moazzen, and Isa Yildirim. "An Approximate Spectral Clustering Ensemble for High Spatial Resolution Remote-Sensing Images." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, no. 5 (2015): 1996–2004. http://dx.doi.org/10.1109/jstars.2015.2424292.

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25

Wang, Peng, Gong Zhang, Siyuan Hao, and Liguo Wang. "Improving Remote Sensing Image Super-Resolution Mapping Based on the Spatial Attraction Model by Utilizing the Pansharpening Technique." Remote Sensing 11, no. 3 (2019): 247. http://dx.doi.org/10.3390/rs11030247.

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The spatial distribution information of remote sensing images can be derived by the super-resolution mapping (SRM) technique. Super-resolution mapping, based on the spatial attraction model (SRMSAM), has been an important SRM method, due to its simplicity and explicit physical meanings. However, the resolution of the original remote sensing image is coarse, and the existing SRMSAM cannot take full advantage of the spatial–spectral information from the original image. To utilize more spatial–spectral information, improving remote sensing image super-resolution mapping based on the spatial attra

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26

Bishop, Michael P., Jeffrey S. Kargel, Hugh H. Kieffer, David J. MacKinnon, Bruce H. Raup, and John F. Shroder. "Remote-sensing science and technology for studying glacier processes in high Asia." Annals of Glaciology 31 (2000): 164–70. http://dx.doi.org/10.3189/172756400781820147.

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AbstractA large number of multispectral and stereo-image data are expected to become available as part of the Global Land Ice Measurements from Space project. We investigate digital elevation model extraction, anisotropic reflectance correction and selected glacier analysis tasks that must be developed to achieve full utility of these new data. Results indicate that glaciers in the Karakoram and Nanga Parbat Himalaya, northern Pakistan, exhibit unique spectral, spatial and geomorphometric patterns that can be exploited by various models and algorithms to produce accurate information regarding

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27

Zheng, Cao, Lv, and Benediktsson. "Spatial–Spectral Feature Fusion Coupled with Multi-Scale Segmentation Voting Decision for Detecting Land Cover Change with VHR Remote Sensing Images." Remote Sensing 11, no. 16 (2019): 1903. http://dx.doi.org/10.3390/rs11161903.

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In this article, a novel approach for land cover change detection (LCCD) using very high resolution (VHR) remote sensing images based on spatial–spectral feature fusion and multi-scale segmentation voting decision is proposed. Unlike other traditional methods that have used a single feature without post-processing on a raw detection map, the proposed approach uses spatial–spectral features and post-processing strategies to improve detecting accuracies and performance. Our proposed approach involved two stages. First, we explored the spatial features of the VHR remote sensing image to complemen

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28

Liu, Xiao Li. "Object Oriented Information Classification of Remote Sensing Image Based on Segmentation and Merging." Applied Mechanics and Materials 568-570 (June 2014): 734–39. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.734.

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The spectral characteristic to classify the remote sensing image classification methods based on pixels of tradition, and the object oriented classification method besides the spectral information, texture feature, also includes the spatial structure of images and other information, so the classification accuracy is very high. In this paper, the remote sensing image based on object oriented classification, puts forward the classification of remote sensing image segmentation based on multiple information combination. Experiments show that, this method can overcome the pixel maximum likelihood c

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29

Li, Sitao, Zhaoming Wang, Shegang Shao, Liuyang Fang, Dan Wang, and Zhiqiang Liu. "Analysis on the Applicability of High-resolution Remote Sensing Images for Highway Construction." Journal of Physics: Conference Series 2031, no. 1 (2021): 012008. http://dx.doi.org/10.1088/1742-6596/2031/1/012008.

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Abstract This paper first analyzes and determines the scope and contents of highway engineering environmental supervision from the aspects of highway engineering composition, construction stage division, impact environmental factors and their characteristics, environmental protection requirements, etc., combines with the characteristics of high-resolution remote sensing images, proposes the construction process and supervision precision requirements for the highway engineering in which environment protection control can be carried out by using satellite remote-sensing images and UAV (unmanned

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30

Feng, Xiaoxiao, Luxiao He, Qimin Cheng, Xiaoyi Long, and Yuxin Yuan. "Hyperspectral and Multispectral Remote Sensing Image Fusion Based on Endmember Spatial Information." Remote Sensing 12, no. 6 (2020): 1009. http://dx.doi.org/10.3390/rs12061009.

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Hyperspectral (HS) images usually have high spectral resolution and low spatial resolution (LSR). However, multispectral (MS) images have high spatial resolution (HSR) and low spectral resolution. HS–MS image fusion technology can combine both advantages, which is beneficial for accurate feature classification. Nevertheless, heterogeneous sensors always have temporal differences between LSR-HS and HSR-MS images in the real cases, which means that the classical fusion methods cannot get effective results. For this problem, we present a fusion method via spectral unmixing and image mask. Conside

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31

Teffahi, H., and N. Teffahi. "EMAP-DCNN: A NOVEL MATHEMATICAL MORPHOLOGY AND DEEP LEARNING COMBINED FRAMEWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 479–86. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-479-2020.

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Abstract. The classification of hyperspectral image (HSI) with high spectral and spatial resolution represents an important and challenging task in image processing and remote sensing (RS) domains due to the problem of computational complexity and big dimensionality of the remote sensing images. The spatial and spectral pixel characteristics have crucial significance for hyperspectral image classification and to take into account these two types of characteristics, various classification and feature extraction methods have been developed to improve spectral-spatial classification of remote sen

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32

Zhao, Jingzheng, Liyuan Wang, Hui Yang, et al. "A Land Cover Classification Method for High-Resolution Remote Sensing Images Based on NDVI Deep Learning Fusion Network." Remote Sensing 14, no. 21 (2022): 5455. http://dx.doi.org/10.3390/rs14215455.

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High-resolution remote sensing (HRRS) images have few spectra, low interclass separability and large intraclass differences, and there are some problems in land cover classification (LCC) of HRRS images that only rely on spectral information, such as misclassification of small objects and unclear boundaries. Here, we propose a deep learning fusion network that effectively utilizes NDVI, called the Dense-Spectral-Location-NDVI network (DSLN). In DSLN, we first extract spatial location information from NDVI data at the same time as remote sensing image data to enhance the boundary information. T

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33

Luo, Xiao Qing, and Xiao Jun Wu. "Fusing Remote Sensing Images Using a Statistical Model." Applied Mechanics and Materials 263-266 (December 2012): 416–20. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.416.

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Enhance spectral fusion quality is the one of most significant targets in the field of remote sensing image fusion. In this paper, a statistical model based fusion method is proposed, which is the improved method for fusing remote sensing images on the basis of the framework of Principal Component Analysis(PCA) and wavelet decomposition-based image fusion. PCA is applied to the source images. In order to retain the entropy information of data, we select the principal component axes based on entropy contribution(ECA). The first entropy component and panchromatic image(PAN) are performed a multi

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34

Weber, I., A. Jenal, C. Kneer, and J. Bongartz. "GYROCOPTER-BASED REMOTE SENSING PLATFORM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 30, 2015): 1333–37. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-1333-2015.

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In this paper the development of a lightweight and highly modularized airborne sensor platform for remote sensing applications utilizing a gyrocopter as a carrier platform is described. The current sensor configuration consists of a high resolution DSLR camera for VIS-RGB recordings. As a second sensor modality, a snapshot hyperspectral camera was integrated in the aircraft. Moreover a custom-developed thermal imaging system composed of a VIS-PAN camera and a LWIR-camera is used for aerial recordings in the thermal infrared range. Furthermore another custom-developed highly flexible imaging sy

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35

Li, Feiyan. "Assessment of Multisource Remote Sensing Image Fusion by several dissimilarity Methods." Journal of Physics: Conference Series 2031, no. 1 (2021): 012016. http://dx.doi.org/10.1088/1742-6596/2031/1/012016.

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Abstract Recently, advancements in remote sensing technology have made it easier to obtain various temporal and spatial resolution satellite data. Remote sensing techniques can be a useful tool to detect vegetation and soil conditions, monitor crop diseases and natural disaster prevention, etc. Although the same scene taken by different sensors belong to the same ground object, the information that they offered are redundant, complementary and collaborative due to the spatial, spectral and temporal resolution are different. The method of image fusion can integrate an image with rich details an

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36

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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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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Koeva, Mila, Rohan Bennett, and Claudio Persello. "Remote Sensing for Land Administration 2.0." Remote Sensing 14, no. 17 (2022): 4359. http://dx.doi.org/10.3390/rs14174359.

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Contemporary land administration (LA) systems incorporate the concepts of cadastre and land registration. Conceptually, LA is part of a global land management paradigm incorporating LA functions such as land value, land tenure, land development, and land use. The implementation of land-related policies integrated with well-maintained spatial information reflects the aim set by the United Nations to deliver tenure security for all (Sustainable Development Goal target 1.4, amongst many others). Innovative methods for data acquisition, processing, and maintaining spatial information are needed in

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Zhou, Hui, and Hongmin Gao. "Fusion Method for Remote Sensing Image Based on Fuzzy Integral." Journal of Electrical and Computer Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/437939.

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This paper presents a kind of image fusion method based on fuzzy integral, integrated spectral information, and 2 single factor indexes of spatial resolution in order to greatly retain spectral information and spatial resolution information in fusion of multispectral and high-resolution remote sensing images. Firstly, wavelet decomposition is carried out to two images, respectively, to obtain wavelet decomposition coefficients of the two image and keep coefficient of low frequency of multispectral image, and then optimized fusion is carried out to high frequency part of the two images based on

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Levy, Joseph, Anne Nolin, Andrew Fountain, and James Head. "Hyperspectral measurements of wet, dry and saline soils from the McMurdo Dry Valleys: soil moisture properties from remote sensing." Antarctic Science 26, no. 5 (2014): 565–72. http://dx.doi.org/10.1017/s0954102013000977.

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AbstractSoil moisture is a spatially heterogeneous quantity in the McMurdo Dry Valleys of Antarctica that exerts a large influence on the biological community and on the thermal state of Dry Valleys permafrost. The goal of this project was to determine whether hyperspectral remote sensing techniques could be used to determine soil moisture conditions in the Dry Valleys. We measured the spectral reflectance factors of wetted soil samples from the Dry Valleys under natural light conditions and related diagnostic spectral features to surface layer soil moisture content. Diagnostic water absorptio

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Magiera, Janusz. "Can Satellite Remote Sensing be Applied in Geological Mapping in Tropics?" E3S Web of Conferences 35 (2018): 02004. http://dx.doi.org/10.1051/e3sconf/20183502004.

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Remote sensing (RS) techniques are based on spectral data registered by RS scanners as energy reflected from the Earth’s surface or emitted by it. In “geological” RS the reflectance (or emittence) should come from rock or sediment. The problem in tropical and subtropical areas is a dense vegetation. Spectral response from the rocks and sediments is gathered only from the gaps among the trees and shrubs. Images of high resolution are appreciated here, therefore. New generation of satellites and scanners (Digital Globe WV2, WV3 and WV4) yield imagery of spatial resolution of 2 m and up to 16 spe

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Mhangara, Paidamwoyo, Willard Mapurisa, and Naledzani Mudau. "Comparison of Image Fusion Techniques Using Satellite Pour l’Observation de la Terre (SPOT) 6 Satellite Imagery." Applied Sciences 10, no. 5 (2020): 1881. http://dx.doi.org/10.3390/app10051881.

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Preservation of spectral and spatial information is an important requirement for most quantitative remote sensing applications. In this study, we use image quality metrics to evaluate the performance of several image fusion techniques to assess the spectral and spatial quality of pansharpened images. We evaluated twelve pansharpening algorithms in this study; the Local Mean and Variance Matching (IMVM) algorithm was the best in terms of spectral consistency and synthesis followed by the ratio component substitution (RCS) algorithm. Whereas the IMVM and RCS image fusion techniques showed better

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Dou, Xinyu, Chenyu Li, Qian Shi, and Mengxi Liu. "Super-Resolution for Hyperspectral Remote Sensing Images Based on the 3D Attention-SRGAN Network." Remote Sensing 12, no. 7 (2020): 1204. http://dx.doi.org/10.3390/rs12071204.

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Hyperspectral remote sensing images (HSIs) have a higher spectral resolution compared to multispectral remote sensing images, providing the possibility for more reasonable and effective analysis and processing of spectral data. However, rich spectral information usually comes at the expense of low spatial resolution owing to the physical limitations of sensors, which brings difficulties for identifying and analyzing targets in HSIs. In the super-resolution (SR) field, many methods have been focusing on the restoration of the spatial information while ignoring the spectral aspect. To better res

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Zhao, Ji, Yanfei Zhong, Xin Hu, Lifei Wei, and Liangpei Zhang. "A robust spectral-spatial approach to identifying heterogeneous crops using remote sensing imagery with high spectral and spatial resolutions." Remote Sensing of Environment 239 (March 2020): 111605. http://dx.doi.org/10.1016/j.rse.2019.111605.

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Gao, Yunhao, Xiukai Song, Wei Li, et al. "Fusion Classification of HSI and MSI Using a Spatial-Spectral Vision Transformer for Wetland Biodiversity Estimation." Remote Sensing 14, no. 4 (2022): 850. http://dx.doi.org/10.3390/rs14040850.

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The rapid development of remote sensing technology provides wealthy data for earth observation. Land-cover mapping indirectly achieves biodiversity estimation at a coarse scale. Therefore, accurate land-cover mapping is the precondition of biodiversity estimation. However, the environment of the wetlands is complex, and the vegetation is mixed and patchy, so the land-cover recognition based on remote sensing is full of challenges. This paper constructs a systematic framework for multisource remote sensing image processing. Firstly, the hyperspectral image (HSI) and multispectral image (MSI) ar

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Ren, Yuanyuan, Xianfeng Zhang, Yongjian Ma, et al. "Full Convolutional Neural Network Based on Multi-Scale Feature Fusion for the Class Imbalance Remote Sensing Image Classification." Remote Sensing 12, no. 21 (2020): 3547. http://dx.doi.org/10.3390/rs12213547.

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Remote sensing image segmentation with samples imbalance is always one of the most important issues. Typically, a high-resolution remote sensing image has the characteristics of high spatial resolution and low spectral resolution, complex large-scale land covers, small class differences for some land covers, vague foreground, and imbalanced distribution of samples. However, traditional machine learning algorithms have limitations in deep image feature extraction and dealing with sample imbalance issue. In the paper, we proposed an improved full-convolution neural network, called DeepLab V3+, w

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Pereira, Eveline, Eduarda Silveira, Inácio Thomaz Bueno, and Fausto Weimar Acerbi Júnior. "Spatial and spectral remote sensing features to detect deforestation in Brazilian Savannas." Advances in Forestry Science 6, no. 4 (2019): 775. http://dx.doi.org/10.34062/afs.v6i4.7525.

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The Brazilian Savannas have been under increasing anthropic pressure for many years, and land-use/land-cover changes (LULCC) have been largely neglected. Remote sensing provides useful tools to detect changes, but previous studies have not attempted to separate the effects of phenology from deforestation, clearing or fires to improve the accuracy of change detection without a dense time series. The scientific questions addressed in this study were: how well can we differentiate seasonal changes from deforestation processes combining the spatial and spectral information of bi-temporal (normaliz

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Wang, Guizhou, Jianbo Liu, and Guojin He. "A Method of Spatial Mapping and Reclassification for High-Spatial-Resolution Remote Sensing Image Classification." Scientific World Journal 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/192982.

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This paper presents a new classification method for high-spatial-resolution remote sensing images based on a strategic mechanism of spatial mapping and reclassification. The proposed method includes four steps. First, the multispectral image is classified by a traditional pixel-based classification method (support vector machine). Second, the panchromatic image is subdivided by watershed segmentation. Third, the pixel-based multispectral image classification result is mapped to the panchromatic segmentation result based on a spatial mapping mechanism and the area dominant principle. During the

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Li, C. K., W. Fang, and X. J. Dong. "Research On The Classification Of High Resolution Image Based On Object-oriented And Class Rule." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W4 (June 26, 2015): 75–80. http://dx.doi.org/10.5194/isprsarchives-xl-7-w4-75-2015.

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With the development of remote sensing technology, the spatial resolution, spectral resolution and time resolution of remote sensing data is greatly improved. How to efficiently process and interpret the massive high resolution remote sensing image data for ground objects, which with spatial geometry and texture information, has become the focus and difficulty in the field of remote sensing research. An object oriented and rule of the classification method of remote sensing data has presents in this paper. Through the discovery and mining the rich knowledge of spectrum and spatial characterist

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Li, Weisheng, Xuesong Liang, and Meilin Dong. "MDECNN: A Multiscale Perception Dense Encoding Convolutional Neural Network for Multispectral Pan-Sharpening." Remote Sensing 13, no. 3 (2021): 535. http://dx.doi.org/10.3390/rs13030535.

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With the rapid development of deep neural networks in the field of remote sensing image fusion, the pan-sharpening method based on convolutional neural networks has achieved remarkable effects. However, because remote sensing images contain complex features, existing methods cannot fully extract spatial features while maintaining spectral quality, resulting in insufficient reconstruction capabilities. To produce high-quality pan-sharpened images, a multiscale perception dense coding convolutional neural network (MDECNN) is proposed. The network is based on dual-stream input, designing multisca

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