Hyperspectral Remote Sensing Image Classification Algorithm Based on Nonlocal Mode Feature Fusion

Aiming at the characteristics of high dimensionality of the hyperspectral image data, nonlincarity of the feature and difficulty of obtaining the tag data, combined with the stack sparse automatic coding network, we propose a two-level classification algorithm based on nonlocal mode feature fusion. Compared with the traditional stack sparse automatic coding network, the spectral angle matching algorithm stacks the spectral information found most similar to the classified pixel to form new spectral information, and puts it into the SoftMax classifier for first-level classification. The pixels satisfying the condition arc added to the training data set for classification training of the stack sparse coding network. Finally, the classification algorithm is modified according to the spatial neighborhood information to make the classification result more smooth. Compared with other classification algorithms, it is found that the improved classification algorithm has higher accuracy and can effectively improve the classification effect of hyperspectral image.