Spectral-Spatial Hyperspectral Image Classification via Boundary-Adaptive Deep Learning

Abstract

Deep learning based hyperspectral image (HSI) classification have recently shown promising performance. However, complex network architecture, tedious training process and effective utilization of spatial/contextual information in deep network limits the application and performance of deep learning. In this paper, for an effective spectral-spatial feature extraction , an improved deep network, spatial adaptive network (SANet) approach is proposed which exploits spatial contextual information and spectral characteristics to construct a more simplified deep network which leads to more powerful feature representation for effective HSI classification. SANet is established from the simple structure of a principal component analysis network. First spatial structural information is extracted and combined with informative spectral channels followed by an object-level classification using SANet based decision fusion approach. It integrates spatial-contextual outcome and spectral characteristics into a SANet framework for robust spectral-spatial HSI classification. Integration of local structural regularity and spectral similarity into simplified deep SANet has significant effect on the classification performance. Experimental results on popular standard HSI datasets reveal that proposed SANet technique produce better classification results than existing well known techniques.