Superpixel-Based Nonnegative Tensor Factorization for Hyperspectral Unmixing

Abstract

Hyperspectral unmixing aims at decomposing a hyperspectral image (HSI) into a number of constituted materials and associated proportions. Recently, nonnegative tensor factorization (NTF) based methods have been proved effective and natural for hyperspectral unmixing owing to their virtue of representing an HSI without any information loss. However, these methods take an HSI as a whole, partly ignoring the local information in distinct local regions. In addition, HSIs are high likely to be disturbed by various noise, making the global information unnecessarily reliable. To alleviate these drawbacks, we propose a superpixel-based matrix-vector nonnegative tensor factorization (S-MV-NTF) method for hyperspectral unmixing, where both the global information and local information are taken into consideration. In this method, the HSI is firstly partitioned into numerous superpixels, homogeneous regions with adaptive sizes and compact boundaries, representing the local spatial structure information. Then, such local information is integrated to the tensor factorization to make the pixels lying in the same superpixel share similar abundances. Experimental results on synthetic data and real-world data show that the proposed method dominates the state-of-the-art methods.