Tensor morphological profile for hyperspectral image classification

Abstract

This paper proposes a novel multi-dimensional morphology descriptor, tensor morphology profile (TMP), for hyperspectral image classification. TMP is a general framework to extract the multi-dimensional structures in high-dimensional data. The nth-order morphology profile is proposed to work with the nth-order tensor, which can capture the inner high order structures. This is different with the traditional mathematical morphology operations which are usually limited to two-dimensional data. By treating hyperspectral images a tensor, it is possible to extend the morphology to high dimensional data so that the powerful morphological tools can be used to analyze the hyperspectral images with spectral-spatial information fused. Experimental results on two commonly used hyperspectral images show that the tensor morphological profile consistently performs better than the extended morphological profile for hyperspectral image classification.