Hyperspectral Image Classification Via Spectral-Spatial Random Patches Network

Abstract

Hyperspectral imageclassification is one of the most important steps in HSI analysis and challenging task for hyperspectral data processing, hyperspectral image contains rich spatial and spectral information. The abundance of spectral and spatial information is helpful to improve the classification accuracy. In this article, we propose a spectral-spatial random patches network (SSRPNet), which directly regards the random patches taken from the image as the convolution kernels without any training. The spectral-spatial feature extracted by SSRPNet stacked into a high dimensional vector, which combined with shallow, deep, spectral, spatial feature. Then, the high dimensional vector is fed into graph-based learning methods for classification, which can achieve excellent classification performance by randomly selecting a subset of features from a small sample point to create a graph. Experimental results on three datasets show that the proposed method can achieve satisfactory classification results compared with closely related methods.

Keywords

• Deep learning
• hyperspectral image classification
• local binary pattern
• random patches network