Unsupervised Band Selection Method Based on Importance-Assisted Column Subset Selection [PDF]
IEEE Access, 2019 Band selection is an important preprocessing technique for hyperspectral images to select a band subset with representative information and low correlation. However, most methods focus on removing redundant components without loss of original information,
Xiaoyan Luo +3 more
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Multiobjective Optimization-Based Hyperspectral Unsupervised Band Selection for Anomaly Detection
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingBand selection (BS) is a critical topic in hyperspectral image dimensionality reduction, focusing on identifying representative bands that can convey the essential information of the full bands without significant loss.
Shihui Liu +4 more
doaj +2 more sources
Segmented Autoencoders for Unsupervised Embedded Hyperspectral Band Selection [PDF]
2018 7th European Workshop on Visual Information Processing (EUVIP), 2018 One of the major challenges in hyperspectral imaging (HSI) is the selection of the most informative wavelengths within the vast amount of data in a hypercube. Band selection can reduce the amount of data and computational cost as well as counteracting the negative effects of redundant and erroneous information. In this paper, we propose an unsupervised,
Tschannerl, Julius +3 more
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Robust Unsupervised Hyperspectral Band Selection via Global Affinity Matrix Reconstruction
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Unsupervised band selection is fundamental to alleviate the curse of dimensionality for hyperspectral imagery. Although many research works have been developed, it is still a challenging problem to improve the poor classification performance with a small
Mengbo You +3 more
doaj +1 more source
Unsupervised hyperspectral band selection using parallel processing [PDF]
2009 IEEE International Geoscience and Remote Sensing Symposium, 2009 Band selection is a common technique to reducing the data dimensionality of hyperspectral imagery. When the desired object information is unknown, the objective of an unsupervised band selection approach is to select the most distinctive and informative bands. Although band selection can significantly alleviate the computational burden in the following
He Yang, Qian Du 0001
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Bathymetric-Based Band Selection Method for Hyperspectral Underwater Target Detection
Remote Sensing, 2021 Band selection has imposed great impacts on hyperspectral image processing in recent years. Unfortunately, few existing methods are proposed for hyperspectral underwater target detection (HUTD).
Jiahao Qi +6 more
doaj +1 more source
Band Ranking via Extended Coefficient of Variation for Hyperspectral Band Selection
Remote Sensing, 2020 Hundreds of narrow bands over a continuous spectral range make hyperspectral imagery rich in information about objects, while at the same time causing the neighboring bands to be highly correlated.
Peifeng Su +2 more
doaj +1 more source
EBARec-BS: Effective Band Attention Reconstruction Network for Hyperspectral Imagery Band Selection
Remote Sensing, 2021 Hyperspectral band selection (BS) is an effective means to avoid the Hughes phenomenon and heavy computational burden in hyperspectral image processing.
Yufei Liu +3 more
doaj +1 more source
Unsupervised Hyperspectral Band Selection using Clustering and Single-Layer Neural Network
Revue Française de Photogrammétrie et de Télédétection, 2018 Hyperspectral images provide rich spectral details of the observed scene by exploiting contiguous bands. But, the processing of such images becomes heavy, due to the high dimensionality.
Mateus Habermann +2 more
doaj +1 more source
A Local Potential-Based Clustering Algorithm for Unsupervised Hyperspectral Band Selection
IEEE Access, 2019 Unsupervised band selection plays an increasingly important role in a hyperspectral image (HSI) classification because of inadequate labeling samples.
Zhaokui Li +5 more
doaj +1 more source