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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Veröffentlicht in:IEEE geoscience and remote sensing letters Jg. 16; H. 12; S. 1889 - 1893
Hauptverfasser: Zeng, Meng, Cai, Yaoming, Cai, Zhihua, Liu, Xiaobo, Hu, Peng, Ku, Junhua
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.12.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
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Zusammenfassung: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. However, a few of them consider the spectral and spatial relationship simultaneously. In this letter, we proposed a novel clustering-based band selection approach using deep subspace clustering (DSC). The proposed approach combines the subspace clustering task into a convolutional autoencoder by treating it as a self-expressive layer, enabling it to be trained end to end. The resulting network can fully extract the interaction of spectral bands based on using spatial information and nonlinear feature transformation. We compared the results of the proposed method with existing band selection methods for three widely used HSI data sets, showing that the proposed method is able to accurately select an informative band subset with remarkable classification accuracy.
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2019.2912170