关键词: 高光谱图像, 深度学习, 多尺度空间特征, 流形学习

Abstract: In view of the problem of dimension “disaster”, insufficient use of spatial information and features in hyperspectral images, combined with the recent advances in deep learning, manifold learning and multiscale spatial features, a TSNE and multiscale sparse auto-encoder neural network hyperspectral image classification algorithm is proposed. TSNE algorithm is used to reduce the hyperspectral image, and then multiscale spatial information extraction is carried out for each pixel’s neighborhood. A sparse auto-encoder network is trained by using the spatial spectrum joint information and classified by softmax classifier, which reduces computational complexity and improves classification accuracy. Experiments on the data of Indian Pines and Pavia University show that the proposed algorithm has better classification results than the other five algorithms.

Key words: hyperspectral image, deep learning, multiscale spatial feature, manifold learning