An Efficient Hyperspectral Image Classification Method Using Deep Fusion of 3-D Discrete Wavelet Transform and CNN

For the predominant classification performance, the convolutional neural network (CNN) is becoming quite popular in hyperspectral image (HSI) classification. However, quite a few parameters must be updated in the training procedure. The overfitting problem is also exacerbated by HSI's high dimensionality and limited training samples. Therefore, an end-to-end model DWTCNN that deeply integrates 3-D discrete wavelet transform (DWT) and CNN is proposed. The 3-D DWT is introduced for the intrinsic feature collection in multi-resolution, and the 3-D DWT modulated kernel with predefined parameters will not increase the trainable parameters of DWTCNN. The impact of 3D-DWT at different scale levels on classification performance is also studied. The amalgamation of 3-D DWT reduces the feature extraction burden of 3-D CNN while increasing the feature extraction capacity of the network. With a compact and light structure, our model is easier to train with less memory and limited training samples. Compared with other methods, our model has a considerable advantage in computational efficiency while maintaining good classification accuracy and clear physical meaning interpretability. Experimental results on three public datasets also demonstrate the superiority of our approach in the training process and classification performance.