Hyperspectral Image Classification Based on Nonlinear Spectral Spatial Network
Recently, for the task of hyperspectral image classification, deep learning based methods have revealed promising performance. However, the complex network structure and the time consuming training process have restricted their applications. In this letter, we construct a much simpler network, i.e. the nonlinear spectral spatial network (NSSNet) for hyperspectral image classification. NSSNet is developed from the basic structure of a principal component analysis network. Nonlinear information is included in NSSNet, to generate a more discriminative feature expression. Moreover, spectral and spatial features are combined to further improve the classification accuracy. Experimental results indicate that our method achieves better performance than state of the art deep learning based methods.
IEEE Geoscience and Remote Sensing Letters