Non-linear spectral unmixing of hyperspectral data using Modified PPNMM

Abstract

Spectral unmixing is one of the unique advantages of hyperspectral images to map the type of species. Such images contain a high spectral resolution making it a classical problem of signal processing at each pixel, which is supposedly formed by the interaction of variously constituted end-members (also known as mixed pixels). Finding the abundance of any feature (or class or end-member) may require these mixed pixels to be unmixed through mixing models. This study proposes a linear mixing model and a non-linear mixing model combined for spectral unmixing and suggests a modified mixing model. We proposed linearly unmixed abundances to be used as prior probabilities for non-linear mixing models. We have applied these methods to synthetic data to check performance and robustness. Synthetic data was created using the reflectance spectra of various end-members collected in the study region through rigorous field surveys. Abundance accuracy, reconstruction accuracy, and other statistical measures were used to assess overall accuracy, with results showing that Modified PPNMM performs better than PPNMM and LMM. The performance outcome is further validated with a satellite dataset (hyperspectral data of Hyperion) with randomly distributed points.