Hyperspectral unmixing plays a fundamental role in mining meaningful information from hyperspectral data. It promotes advancements in various scientific, environmental, and industrial applications by extracting meaningful information from hyperspectral data. However, it is still hindered by several challenges, including accurately identifying the number of endmembers in a hyperspectral image, extracting the endmembers, and estimating their abundance fractions. This research addresses these challenges by employing …