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Hyperspectral band selection: A review
A hyperspectral imaging sensor collects detailed spectral responses from ground objects
using hundreds of narrow bands; this technology is used in many real-world applications …
using hundreds of narrow bands; this technology is used in many real-world applications …
An extensive review of hyperspectral image classification and prediction: techniques and challenges
Abstract Hyperspectral Image Processing (HSIP) is an essential technique in remote
sensing. Currently, extensive research is carried out in hyperspectral image processing …
sensing. Currently, extensive research is carried out in hyperspectral image processing …
Deep neural networks-based relevant latent representation learning for hyperspectral image classification
The classification of hyperspectral image is a challenging task due to the high dimensional
space, with large number of spectral bands, and low number of labeled training samples. To …
space, with large number of spectral bands, and low number of labeled training samples. To …
Optimal clustering framework for hyperspectral band selection
Band selection, by choosing a set of representative bands in a hyperspectral image, is an
effective method to reduce the redundant information without compromising the original …
effective method to reduce the redundant information without compromising the original …
Hyperspectral anomaly detection with relaxed collaborative representation
Anomaly detection has become an important remote sensing application due to the
abundant spectral and spatial information contained in hyperspectral images. Recently …
abundant spectral and spatial information contained in hyperspectral images. Recently …
Salient band selection for hyperspectral image classification via manifold ranking
Saliency detection has been a hot topic in recent years, and many efforts have been devoted
in this area. Unfortunately, the results of saliency detection can hardly be utilized in general …
in this area. Unfortunately, the results of saliency detection can hardly be utilized in general …
Spatial and spectral structure preserved self-representation for unsupervised hyperspectral band selection
C Tang, J Wang, X Zheng, X Liu, W **-Jia-2/publication/260500005_Feature_Mining_for_Hyperspectral_Image_Classification/links/54dbddd30cf28d3de65df30e/Feature-Mining-for-Hyperspectral-Image-Classification.pdf" data-clk="hl=iw&sa=T&oi=gga&ct=gga&cd=7&d=5547378591719028170&ei=JIHFZ7vGOueuieoP0ZismQs" data-clk-atid="yvUEGnA__EwJ" target="_blank">[PDF] researchgate.net
Feature mining for hyperspectral image classification
Hyperspectral sensors record the reflectance from the Earth's surface over the full range of
solar wavelengths with high spectral resolution. The resulting high-dimensional data contain …
solar wavelengths with high spectral resolution. The resulting high-dimensional data contain …
Hyperspectral band selection via adaptive subspace partition strategy
Band selection is considered as a direct and effective method to reduce redundancy, which
is to select some informative and distinctive bands from the original hyperspectral image …
is to select some informative and distinctive bands from the original hyperspectral image …
Robust dual graph self-representation for unsupervised hyperspectral band selection
Unsupervised band selection aims to select informative spectral bands to preprocess
hyperspectral images (HSIs) without using labels. Traditional band selection methods only …
hyperspectral images (HSIs) without using labels. Traditional band selection methods only …