Hyper-voxel based deep learning for hyperspectral image classification

Abstract

Classification of distinct classes in hyperspectral images (HSI) is one of the most pervasive problem in remote sensing field. Deep learning has recently proved its efficiency in HSI classification. However, incorporating spatial/contextual features along with spectral information in deep network is still a challenging task. In this paper, for an effective spectral-spatial feature extraction, an improved deep network, spatial updated hyper-voxel stacked auto-encoder (HVSAE) approach is proposed which exploits spatial context within spectrally similar contiguous pixels for effective HSI classification. The proposed approach involves two key steps-firstly, we compute adaptive boundary adjustment based segmentation whose size and shape can be adapted according to the spatial structures and which consists of spatially contiguous pixels with similar spectral features, followed by an object-level classification using stacked auto-encoder (SAE) based decision fusion approach that merges spatial-segmented outcome and spectral information into a SAE framework for robust spectral-spatial HSI classification. In addition, instead of directly using a large number of spectral bands, band preference and correlation based band selection approach is used to select the most informative bands without compromising the original content in HSI. Use of local spatial structural regularity and spectral similarity information from adaptive boundary adjustment based process, and fusion of spatial context and spectral features into SAE has significant effect on the accuracy of the final HSI classification. Experimental results on real divergent hyperspectral imagery with different contexts and resolutions validates the classification accuracy of the proposed method over several existing techniques.