Evaluation of Hyperspectral Unmixing Methods: A Comparative Study for Very-High Spatial Resolution Hyperspectral Images

Hyperspectral unmixing (HU) in Hyperspectral Imaging (HSI) analyzes and breaks down the constituent components within a pixel, providing insights into the object composition within subpixel regions. The availability of diverse methodologies for endmember extraction (EE) are available for HU. Recent work shows the potential value of HU in Very High Spatial Resolution Hyperspectral Images (VHSR-HSI), enabling spectral signature extraction, capturing spectral variability and extracting material spatial distribution. In this study, we aim to study the effectiveness of traditional endmember extraction algorithms for HU within the realm of VHSR-HSI. Preliminary results using and comparing multiple endmember extraction techniques are presented, placing specific emphasis on the efficacy of N-FINDR, Pixel Purity Index (PPI) and a modified version of PPI developed by the authors. Our approach is applied to hyperspectral images obtained from close-range observations using a standoff hyperspectral imager. Results show the effectiveness of NFINDR and modified PPI to extract the spectral signature of the different classes in the image.