An Effective Hyperspectral Image Classification Approach Based on Discrete Wavelet Transform and Dense CNN

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Abstract
Recently, benefiting from the rapid evolution of Deep Learning technology, hyperspectral image (HSI) classification performance is considerably improved. However, the traditional convolutional analysis resulted in too many learnable parameters of the network, and lots of training samples are required to train the network to avoid the overfitting problem. On the other hand, the 3-D discrete wavelet transform can effectively extract both spatial and spectral information, maintaining robust feature representation capabilities and reducing the computational burden of CNN simultaneously. In this letter, the 3-D discrete wavelet transform is adopted to perform preprocessing operations on HSI. Then, the 3D CNN that integrates dense connections attaches great importance to the reuse of features. Furthermore, our approach significantly reduces network parameters, relieves the training complexity of the network, alleviates the training process from overfitting, and improves classification performance with limited training samples. Three benchmark datasets were used for very rigorous HSI classification experiments to test the performance of the proposed approach. The results demonstrate the superiority of the proposed approach compared with existing state-of-the-art (SOTA) HSI classification approaches. The source code is publicly available at https://github.com/xujingran/DWTDENSE.

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