Local Similarity-Based Spatial-Spectral Fusion Hyperspectral Image Classification With Deep CNN and Gabor Filtering

Currently, the different deep neural network (DNN) learning approaches have done much for the classification of hyperspectral images (HSIs), especially most of them use the convolutional neural network (CNN). HSI data have the characteristics of multidimensionality, correlation, nonlinearity, and a large amount of data. Therefore, it is particularly important to extract deeper features in HSIs by reducing dimensionalities which help improve the classification in both spectral and spatial domains. In this article, we present a spatial-spectral HSI classification algorithm, local similarity projection Gabor filtering (LSPGF), which uses local similarity projection (LSP)-based reduced dimensional CNN with a 2-D Gabor filtering algorithm. First, use the local similarity analysis to reduce the dimensionality of the hyperspectral data, and then we use the 2-D Gabor filter to filter the reduced hyperspectral data to generate spatial tunnel information. Second, use the CNN to extract features from the original hyperspectral data to generate spectral tunnel information. Third, the spatial tunnel information and the spectral tunnel information are fused to form the spatial-spectral feature information, which is input into the deep CNN to extract more effective features; and finally, a dual optimization classifier is used to classify the final extracted features. This article compares the performance of the proposed method with other algorithms in three public HSI databases and shows that the overall accuracy of the classification of LSPGF outperforms all datasets.

Automated summary

This article introduces a novel HSI classification algorithm, LSPGF, which utilizes LSP and Gabor filtering to extract deeper features from original hyperspectral data, achieving higher classification accuracy compared to other algorithms.