Unsupervised Hyperspectral Image Band Selection Based on Deep Subspace Clustering

Hyperspectral image (HSI) consists of hundreds of continuous narrow bands with high redundancy, resulting in the curse of dimensionality and an increased computation complexity in HSI classification. Many clustering-based band selection approaches have been proposed to deal with such a problem. Howe...

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Bibliographic Details
Published in:	IEEE geoscience and remote sensing letters Vol. 16; no. 12; pp. 1889 - 1893
Main Authors:	Zeng, Meng, Cai, Yaoming, Cai, Zhihua, Liu, Xiaobo, Hu, Peng, Ku, Junhua
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.12.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Band selection Classification Clustering Clustering algorithms Computation deep convolutional autoencoder (CAE) Feature extraction Geology hyperspectral image (HSI) Hyperspectral imaging Redundancy Sensor phenomena and characterization Spatial data Spectral bands subspace clustering Subspaces
ISSN:	1545-598X, 1558-0571
Online Access:	Get full text
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