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Analysis of Hyperspectral Data to Develop an Approach for Document Images [PDF]
Sensors, 2023 Hyperspectral data analysis is being utilized as an effective and compelling tool for image processing, providing unprecedented levels of information and insights for various applications.
Zainab Zaman +2 more
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DIMENSIONALITY REDUCTION VIA AN ORTHOGONAL AUTOENCODER APPROACH FOR HYPERSPECTRAL IMAGE CLASSIFICATION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 Nowadays, the increasing amount of information provided by hyperspectral sensors requires optimal solutions to ease the subsequent analysis of the produced data.
V. H. Ayma, V. A. Ayma, J. Gutierrez
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Editorial for Special Issue “Advances in Hyperspectral Data Exploitation”
Remote Sensing, 2022 Hyperspectral imaging (HSI) has emerged as a promising, advanced technology in remote sensing and has demonstrated great potential in the exploitation of a wide variety of data.
Chein-I Chang +8 more
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Remote Sensing, 2023 Hyperspectral data usually consists of hundreds of narrow spectral bands and provides more detailed spectral characteristics compared to commonly used multispectral data in remote sensing applications.
Hua Yang +5 more
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Remote Sensing, 2022 ZY1-02D is a Chinese hyperspectral satellite, which is equipped with a visible near-infrared multispectral camera and a hyperspectral camera. Its data are widely used in soil quality assessment, mineral mapping, water quality assessment, etc.
Mingyuan Peng +6 more
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An Efficient Method for Generating UAV-Based Hyperspectral Mosaics Using Push-Broom Sensors
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Hyperspectral sensors mounted in unmanned aerial vehicles offer new opportunities to explore high-resolution multitemporal spectral analysis in remote sensing applications.
Jurado Rodriguez JuanManuel +4 more
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Sparse Unmixing of Hyperspectral Data [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2011 Linear spectral unmixing is a popular tool in remotely sensed hyperspectral data interpretation. It aims at estimating the fractional abundances of pure spectral signatures (also called as endmembers) in each mixed pixel collected by an imaging spectrometer.
Marian-Daniel Iordache +2 more
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Delving Into Classifying Hyperspectral Images via Graphical Adversarial Learning
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Recent remote sensing literature has seen generative adversarial network (GAN)-based models developed for hyperspectral image classification, especially in a spatiospectral manner.
Guangxing Wang, Peng Ren
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Research on dimension reduction method for hyperspectral remote sensing image based on global mixture coordination factor analysis [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015 Over the past thirty years, the hyperspectral remote sensing technology is attracted more and more attentions by the researchers. The dimension reduction technology for hyperspectral remote sensing image data is one of the hotspots in current research ...
S. Wang, C. Wang
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Egyptian Journal of Remote Sensing and Space Sciences, 2022 Accurate extraction of urban impervious surface (UIS) is essential for urban planning and environmental monitoring. However, multispectral remote sensing data for UIS extraction suffers from the inter-class spectral confusions, e.g.
Yinyi Lin +6 more
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