Detection and segmentation in hyperspectral imagery using discriminant analysis

Abstract

Hyper-spectral imagery (HSI) contains significant spectral resolution that enables material identification. Typical methods of classification include various forms of matching sample image spectra to pure end-member sample spectra or mixtures of these end-members. Often, pure end-members are not available a-priori. We propose the use of HSI to complement other sensor modalities which are used to cue the end-member selection process for target detection. Multiple sensor modalities are frequently available and sensor fusion is exploited as demonstrated by the DARPA Dynamic Database (DDB) and Multisensor Exploitation Testbed (MSET) programs. Candidate target pixels, cued from other sensor modalities, are registered to the HSI and verified using local matched filters. Target identification is then performed using multiple methods including Euclidean distance, spectral angle mapping, anomaly detection, principal component analysis (PCA) decomposition and reconstruction, and linear discriminant analysis (LDA). The use of LDA for target identification as well as scene segmentation provides significant capabilities to HSI understanding.