An Enhanced 3-D Discrete Wavelet Transform for Hyperspectral Image Classification

In the classification of hyperspectral image (HSI), there exists a common issue that the collected HSI data set is always contaminated by various noise (e.g., Gaussian, stripe, and deadline), degrading the classification results. To tackle this issue, we modify the 3-dimensional discrete wavelet transform (3DDWT) method by considering the noise effect on feature quality and propose an enhanced 3DDWT (E-3DDWT) approach to extract the feature and meanwhile alleviate the noise. Specifically, the proposed E-3DDWT method first applies classical 3DDWT method to the HSI data cube and thus can generate eight subcubes in each level. Then, the stripe noise is concentrated into several subcubes due to its spatial vertical property. Finally, we abandon these subcubes and obtain the feature cube by stacking the remaining ones. After acquiring the feature, we then adopt the convolutional neural network (CNN) model with an active learning strategy for classification since CNN has been verified to be a state-of-the-art feature extraction method for HSI classification, and active learning strategy can alleviate the insufficient labeled sample issue to some extent. In addition, we apply the Markov random field to enhance the final categorized results. Experiments on two synthetically striped data sets show that our proposed approach achieves better categorized results than other advanced methods.