Abstract: Hyperspectral remote sensing images contains hundreds of spectral bands and rich spectral information, resuling in wideapplications in the classification of ground objects, but there remains the problem of the curse of dimensionality. Hyperspectral images also contain rich texture information which can improve the classification precision significantly. 3D Gabor filter can not only keep rich spectral information of the images, but also extract the image texture features. In order to make full use of the features of hyperspectral images, this paper proposed a hyperspectral image classification model based on 3D Gabor and residual three-dimensional convolution neural network (Res-3DCNN). The 3DCNN can deal with three-dimensional cubic hyperspectral data and extract sufficient texture-spectral information. However, the deepening of convolutional neural network structure will lead to the problem of network degradation. Therefore, the idea of residual learning was applied to the improvement of the performance of 3DCNN. The proposed method was examined with three public hyperspectral data sets of PaviaU, Indian Pines and Salinas, reaching the average classification accuracy of 99.17%, 97.40% and 98.56%, respectively. Experimental results prove that the proposed method can effectively improve the ground targets classification accuracy of hyperspectral images. 

Key words: hyperspectral images classification, convolutional neural network, three-dimensional Gabor filter, three-dimensional convolution, residual learning