Manifold Regularized Sparse Archetype Analysis Considering Endmember Variability

作者：Mingming Xu, Xin Zou, Shanwei Liu, Hui Sheng, Zhiru Yang · 发表于：IEEE Geoscience and Remote Sensing Letters · 年份：2023 · DOI：10.1109/lgrs.2023.3295398 · 被引用次数：6 · 研究领域：Remote-Sensing Image Classification、Advanced Image Fusion Techniques、Remote Sensing and Land Use

Due to the low resolution of hyperspectral images, the problem of mixed pixels is common, and hyperspectral unmixing is a crucial technology to solve the problem of mixed pixels. Among them, nonnegative matrix factorization (NMF) is widely used because it can simultaneously perform endmember and abundance estimations. As a variant of NMF, the archetype analysis (AA) is to find the most representative sample in the dataset, which has strong interpretability compared with NMF. However, traditional AA-based unmixing methods consider only one spectral curve to represent one class, ignoring endmember variability. To solve this problem, a manifold regularized sparse AA unmixing method considering endmember variability is proposed. In this paper, various spectra were included for each class to fully account for variability. In addition, considering the sparsity of abundance, L2,1regularization is used to impose sparse constraints on abundance, which ensures the sparseness of abundance. Furthermore, a manifold regularization constraint is introduced to use the underlying manifold structure of the data in unmixing, the construction of which is done by superpixel segmentation. The close relationship between the original image and the abundance is preserved. Experimental results on both synthetic and real hyperspectral datasets illustrate that the proposed method is superior to several multi-endmember extraction algorithms, AA-based algorithms, and advanced sparse NMF-based algorithms.