Deep Transfer HSI Classification Method Based on Information Measure and Optimal Neighborhood Noise Reduction

Abstract

Land environment is one of the most commonly and importantly used synthetical natural environments in a virtual test. To recognize the ground truth for the construction of virtual land environment, a deep transfer hyperspectral image (HSI) classification method based on information measure and optimal neighborhood noise reduction was proposed in this article. Firstly, the information measure method was used to select the most valuable spectrum. Specifically, three representative bands were selected using the combination of entropy, color matching function, and mutual information. Based on the selected bands, a patch containing spatial-spectral information was constructed and used as the input of the convolutional neural networks (CNN) network. Then, in order to address the problem that a large number of labeled samples were required in deep learning method, the HSI classification method based on deep transfer learning was proposed. In the proposed method, the transfer of parameters ensured the classification performance with small training samples and reduced the training cost. Moreover, the optimal neighborhood noise reduction was used as the post-processing method to effectively eliminate the salt-and-pepper noise and further improve the classification performance. Experiments on two datasets demonstrated that the proposed method in this article had higher classification accuracy than similar methods.