Hyperspectral Classification Via Spatial Context Exploration with Multi-Scale CNN

Abstract

Spatial context has shown to be very useful in hyperspectral image processing. Existing convolutional neural network (CNN)-based methods for hyperspectral classification explore spatial context by single-scale convolution kernels in 2D or 3D shapes. However, such single-scale convolution may not be capable to explore the complex spatial context in a hyperspectral image. In this paper, we propose a multi-scale CNN, MS-CNN to explore the spatial context in different extents, in which adaptive spatial neighborhood convolution kernels are used to simultaneously extract multiple spectral-spatial features from spatial context of pixels. These features obtained by different spatial kernels are then concatenated and fused for further feature extraction and classification. Experimental results show that the proposed adaptive spatial neighborhood convolution are more effective to explore spatial context than traditional single-scale spatial convolution and the performance of the proposed MS-CNN outperforms several state-of-art CNNs for classification of hyperspectral images.