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A Two-Stage Convolutional Sparse Coding Network for Hyperspectral Image Classification
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DOI：10.1109/LGRS.2023.3245210.png)
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Abstract
En 中文
The convolutional sparse coding (CSC) can learn shift-invariant convolution kernels. In deep convolutional neural networks, it takes a lot of time to train the convolution kernels. In this letter, a deep two-stage CSC network (DTCSCNet) is proposed, which can be used to simultaneously extract spatial features and spectral features from hyperspectral image (HSI) without back propagation and fine-tuning process, thus saving a lot of time. Furthermore, to further improve the performance of the network, we incorporate multiscale information. After deep feature extraction using DTCSCNet, we further investigate the classification performance of different classifiers on the extracted features. Experimental results show that the proposed method can obtain better classification performance compared with some closely related HSI classification methods.
Keywords:
Feature extraction
Convolution
Kernel
Convolutional codes
Support vector machines
Principal component analysis
Hyperspectral imaging
Convolutional sparse coding (CSC)
deep learning (DL)
graph-based learning (GSL)
hyperspectral image (HSI) classification
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