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Deep High-Order Tensor Convolutional Sparse Coding for Hyperspectral Image Classification
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DOI：10.1109/TGRS.2021.3134682.png)
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摘要
En 中文
Most hyperspectral image (HSI) data exist in the form of tensor; the tensor representation preserves the potential spatial & x2013;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 & x2013;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.
Keyword:
Feature extraction
Tensors
Convolutional codes
Hyperspectral imaging
Kernel
Image coding
Convolution
Deep high-order tensor convolutional sparse coding (CSC)
deep learning
graph-based learning (GSL)
hyperspectral image (HSI) classification
期刊
IF:
8.6
论文数:
2.1W
被引数:
10.7W
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