An Enhanced Model for Blind Source Separation under Source Variability Condition and its application in Hyperspectral Un mixing
Document Type : Original Article
Authors
1
Department of Electrical Engineering,Shiraz University of Technology, Shiraz, Iran
2
Department of electrical engineering, Shiraz university of Technology, Shiraz, Iran
Abstract
Spectral un mixing is an important topic in hyperspectral imaging as a source of identification and separation of different materials in different wavelengths, which is known as endmember signatures (EMs). Abundance is the proportion of these EMs. The process of extracting two above information is not fixed, because of variation of EMs and nonlinear mixing phenomena. Extended linear mixing model (ELMM) addresses this by allowing per-pixel endmember variations. While ELMM effectively captures certain type of endmember variability, it introduces a multiplicative ambiguity between abundances and scaling factors. Besides, ELMM can be ambiguous when variations arise from microscale material structure rather than changes in the endmembers themselves, or in the mixing of composite materials. To address these limitations, this paper proposes an improved ELMM (IELMM). IELMM considers that each pixel is allowed to vary freely in each frequency layer, as well as changes in relation to other pixels and EMs. Allowing more flexibility in EM structure modeling, leads to introduce a new approach in the area of un mixing based on novel mathematical framework. The new framework considers the source of variation in observation matrix with a 3-dimensional scaling factor array as a source of variation in endmember matrix. Besides, we have developed the required mathematical equations for optimizing blind hyperspectral un mixing results in improvement of precise un mixing and better convergence. Simulation results based on real and synthetic data sets confirm the efficiency of the proposed approach.
Keywords
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Volume 6, Issue 1
September 2026Pages 74-90
- Receive Date 02 May 2025
- Revise Date 29 July 2025
- Accept Date 21 September 2025