Hyperspectral remote sensing can obtain both spatial images and continuous spectral data of land cover, thus realizing the classification and identification of ground objects. However, the high-dimensional characteristics of hyperspectral images pose great challenges to classification. Therefore, this paper discusses a hyperspectral image classification method based on Hash learning. Hash learning can switch high-dimensional information to low-dimensional binary encoding, and achieve classification by calculating encoding internal product and using hamming distance.To effectively express nonlinear data, one supervised hashing method is proposed. However, the disadvantage of Hash learning are that they run slowly and lack consideration for spatial neighborhood information. Therefore, this paper introduces RBF kernel to improve efficiency. In addition, it uses four-dimensional convolution to fully express spatial information, namely Supervised Hashing with RBF Kernel and Convolution (CKSH).Experiments were carried out on the international general test data. Experimental results show that the proposed method is superior to traditional classification methods and other hash learning. Under different percentage conditions of training samples, it has achieved high classification accuracy and reached 96.12% (Indian Pine, 10%) and 98.00% (University of Pavia, 5%), which verified the effectiveness of proposed method.In view of the two problems that loss function of KSH using the L2 norm causes low efficiency, and it does not consider spatial information. This paper uses four-dimensional convolution to introduce spatial information, and applies the RBF kernel instead of the L2 norm. Experiments on general test data sets have confirmed the advantages of CKSH in classification accuracy and runtime. The reason for high accuracy and efficiency has two hands. On the one hand, CKSH uses four-dimensional convolution to mine underlying structural information. Therefore, the obtained binary encoding conducive to improving the classification performance. On the other hand, using the RBF kernel as loss function can significantly reduce the arithmetic series.

remote sensing;hyperspectral image;hash learning;RBF;four-dimensional convolution;feature extraction;spectral-spatial classification