Sparse and Low-Rank Constrained Tensor Factorization for Hyperspectral Image Unmixing

Third-order tensors have been widely used in hyperspectral remote sensing because of their ability to maintain the 3-D structure of hyperspectral images. In recent years, hyperspectral unmixing algorithms based on tensor factorization have emerged, but these decomposition processes may be inconsistent with physical mechanism of unmixing. To solve this problem, this article proposes a sparse and low-rank constrained tensor factorization unmixing algorithm based on a matrix-vector nonnegative tensor factorization (MV-NTF) framework. Considering the fact that each component tensor obtained by the image decomposition contains only one endmember and the corresponding abundance matrix has sparse property, a sparse constraint is imposed to ensure the accuracy of abundance maps. Since abundance maps also have low-rank attribute, in order to avoid the strict low-rank constraint in the original MV-NTF framework, a low-rank tensor regularization is introduced to flexibly express the low-rank characteristics of the abundance tensors, making the resulting abundance maps more in line with the actual scene. Then, the optimization problem is solved by using the alternating direction method of multipliers. In experiments, simulated datasets are adopted to demonstrate the effectiveness of the sparse and low-rank constraints of the proposed algorithm, and real datasets from different sensors and different scenarios are used to verify its applicability.

Automated summary

This article introduces a sparse and low-rank constrained tensor factorization unmixing algorithm based on MV-NTF framework, which aims to improve the accuracy and applicability of abundance maps by imposing sparse constraint and low-rank regularization.