Relevance of transformation techniques in rapid endmember identification and spectral unmixing: A hypespectral remote sensing perspective

Abstract

One of the tedious and time consuming tasks related to hyperspectral data analysis is the identification of library candidates for spectral unmixing. In this study, we evaluated the relevance of different transformation procedures such as First Derivative (FD), Fast Fourier Transform (FFT), Discrete Wavelet Transform (DWT), Hilbert-Huang Transform (HHT) and S-transform (ST) in automated retrieval of library endmembers for linear spectral unmixing. The spectral similarity between the target and library candidates were estimated using Pearson's Correlation Coefficient (PCC) and student t-test based approach. Subsequently, these endmembers are used to estimate the fractional abundances by Fully Constrained Least Square Estimation (FCLSE) based Quadratic Programming (QP) optimization approach. The match between the target and modeled spectrum was calculated based on Root Mean Squared Error (RMSE) and spectral similarity scores estimated using Spectral Angle Mapper (SAM). In addition to RMSE and SAM scores, the simulation processing time and appropriateness of identified endmembers are considered to estimate the effectiveness of each transformation procedure. It is observed that DWT, HHT and ST based approaches are more efficient in identifying correct library endmembers than the FD and FFT based approaches.