Toward Weak Signal Analysis in Hyperspectral Data: An Efficient Unmixing Perspective

Abstract

Many unmixing methods hold the assumption that endmembers correspond to major land-covers, but not true for some unmixing tasks where observed minor object signals corresponding to some special types of endmembers are relatively weak. When there exist weak signals that have low intensity potentially caused by subtle mixing abundance fractions regarding the endmembers of minor objects, the traditional unmixing techniques may fail. This paper pioneers weak signal scenarios in hyperspectral unmixing using an efficient method called HyperWeak. Specifically, HyperWeak involves a sparse nonnegative matrix factorization model that contains two main parts, where the unsupervised part estimates the endmember and abundance matrices, and the supervised part ensures the minimal degradation of prior knowledge. To enhance the robustness of the HyperWeak model, this paper considers a reweighted sparsity constraint to boost the sparseness of the abundance matrix. For effectively solving optimization problems, Nesterov’s optimal gradient method is used in this paper. Experiments conducted on synthetic and real hyperspectral images indicate that HyperWeak can improve the unmixing performances of hyperspectral data in weak signal situations.