Hyperspectral Unmixing Network Accounting for Spectral Variability Based on a Modified Scaled and a Perturbed Linear Mixing Model

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 15; p. 3890
• Main Authors: Cheng, Ying, Zhao, Liaoying, Chen, Shuhan, Li, Xiaorun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2023
• Subjects: Accuracy; Algorithms; Constraint modelling; Deep learning; endmember variability; Environmental conditions; Hyperspectral imaging; hyperspectral unmixing; Image analysis; Image processing; linear mixing model; Machine learning; Methods; Neural networks; Perturbation; Regularization; Remote sensing; Smoothness; Spectral signatures; Variability; variational autoencoder; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15153890

Spectral unmixing is one of the prime topics in hyperspectral image analysis, as images often contain multiple sources of spectra. Spectral variability is one of the key factors affecting unmixing accuracy, since spectral signatures are affected by variations in environmental conditions. These and other factors interfere with the accurate discrimination of source type. Several spectral mixing models have been proposed for hyperspectral unmixing to address the spectral variability problem. The interpretation for the spectral variability of these models is usually insufficient, and the unmixing algorithms corresponding to these models are usually classic unmixing techniques. Hyperspectral unmixing algorithms based on deep learning have outperformed classic algorithms. In this paper, based on the typical extended linear mixing model and the perturbed linear mixing model, the scaled and perturbed linear mixing model is constructed, and a spectral unmixing network based on this model is constructed using fully connected neural networks and variational autoencoders to update the abundances, scales, and perturbations involved in the variable endmembers. Adding spatial smoothness constraints to the scale and adding regularization constraints to the perturbation improve the robustness of the model, and adding sparseness constraints to the abundance determination prevents overfitting. The proposed approach is evaluated on both synthetic and real data sets. Experimental results show the superior performance of the proposed method against other competitors.