Results 11 to 20 of about 24,881,858 (314)
Deep Learning for Classification of Hyperspectral Data: A Comparative Review [PDF]
IEEE Geoscience and Remote Sensing Magazine, 2019 In recent years, deep-learning techniques revolutionized the way remote sensing data are processed. The classification of hyperspectral data is no exception to the rule, but it has intrinsic specificities that make the application of deep learning less ...
N. Audebert +2 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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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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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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Power spectral clustering on hyperspectral data [PDF]
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017 Classification of remotely sensed data is an important task for many practical applications. However, it is not always possible to get the ground truth for supervised learning methods. Thus unsupervised methods form a valuable tool in such situations. Such methods are referred to as clustering methods. There exists several strategies for clustering the
Challa, Aditya +3 more
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1D-CONVOLUTIONAL AUTOENCODER BASED HYPERSPECTRAL DATA COMPRESSION
, 2021 Hyperspectral sensor technology has been advancing in recent years and become more practical to tackle a variety of applications. The arising issues of data transmission and storage can be addressed with the help of compression.
J. Kuester, W. Gross, W. Middelmann
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Hyperspectral Data: Efficient and Secure Transmission [PDF]
Algorithms, 2017 Airborne and spaceborne hyperspectral sensors collect information which is derived from the electromagnetic spectrum of an observed area. Hyperspectral data are used in several studies and they are an important aid in different real-life applications (e.g., mining and geology applications, ecology, surveillance, etc.). A hyperspectral image has a three-
Pizzolante, Raffaele, Carpentieri, Bruno
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Nature Communications, 2023 Hyperspectral imaging is vital for material identification but traditional systems are bulky, hindering the development of compact systems. While previous metasurfaces address volume issues, the requirements of complicated fabrication processes and ...
Chia-Hsiang Lin +3 more
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