Multi-Scale Structure Extraction for Hyperspectral Image Classification

Abstract

In this paper, a novel multi-scale structure extraction based spectral-spatial hyperspectral image classification method is proposed, which consists of the following steps. First, the spectral dimension of the hyperspectral image is reduced by averaging adjacent spectral bands. Then, in order to extract the multi-scale significant structural features (MSFs) which are insensitive to image noise and texture, a relative total variation based structure extraction method is applied on the dimension reduced hyperspectral image. Finally, the MSFs are fused together with the kernel principal component analysis (KPCA), so as to obtain the kernel PCA fused multi-scale structural features (KPCA-MSFs) for classification. Experiments conducted on a real hyperspectral image demonstrate the outstanding performance of the proposed approach over several state-of-the-art spectral-spatial classifiers, especially when the image is corrupted by serious scene noise.