Hyperspectral Image Classification Based on Long Short Term Memory Network

In the task of hyperspectral image classification, how to learn features of hyperspectral image is the important and difficulty issue which may directly affect the classification results. Inspired by the idea of natural language processing, in this paper, we propose a local space long short-term memory network based hyperspectral image classification, which constructs sequential features in the local area of hyperspectral images. This method is based on the integration features of two traditional low-level features, and from these integration features to extract sequential features of the center sample in the local space, then use the long short-term memory network to learn high-level semantic features, finally use them to classify image. This method can not only obtain more representative and discriminative high-level semantic features, and through constructing the local space sequence to enhance positive impact of the useful pixels, inhibit negative effects of useless pixels, it improves the classification accuracy.