Deep High-Order Tensor Convolutional Sparse Coding for Hyperspectral Image Classification

Most hyperspectral image (HSI) data exist in the form of tensor; the tensor representation preserves the potential spatial-spectral structure information compared with the vector representation, which can help improve the classification performance of HSI. In this article, a deep high-order tensor c...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 60; pp. 1 - 11
Main Authors: Cheng, Chunbo, Li, Hong, Peng, Jiangtao, Cui, Wenjing, Zhang, Liming
Format: Journal Article
Language:English
Published: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Classification
Convolution
Convolutional codes
Deep high-order tensor convolutional sparse coding (CSC)
deep learning
Feature extraction
graph-based learning (GSL)
hyperspectral image (HSI) classification
Hyperspectral imaging
Image classification
Image coding
Kernel
Mathematical analysis
Methods
Representations
Spectra
Tensors
ISSN:0196-2892, 1558-0644
Online Access:Get full text
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Summary:Most hyperspectral image (HSI) data exist in the form of tensor; the tensor representation preserves the potential spatial-spectral structure information compared with the vector representation, which can help improve the classification performance of HSI. In this article, a deep high-order tensor convolutional sparse coding (CSC) model is proposed, which can be used to train deep high-order filters. Based on the deep high-order tensor CSC model, a deep feature extraction network (DHTCSCNet) is constructed, which is used for feature extraction of HSIs. By combining the spectral-spatial feature and the features extracted by the proposed DHTCSCNet at each layer, a combined feature that incorporates shallow, deep, spectral, and spatial features can be obtained. Then, the graph-based learning (GSL) methods are used to classify the combined feature. Experimental results show that the DHTCSCNet can obtain better classification performance compared with other HSI classification methods.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2021.3134682