Improved error mitigation in endmember unmixing of hyperspectral images via image partitioning of target-like spectral anomalies

摘要

Hyperspectral images can be conveniently and quickly interpreted by detecting spectral endmembers present in the image and unmixing the image in terms of those endmembers. However, spectral diversity common in hyperspectral images leads to errors in the unmixing process by increasing the likelihood that spectral anomalies will be detected as endmembers. We have developed an algorithm to detect target-like spectral anomalies in the image which are likely to detrimentally interfere with the endmember detection process. The hyperspectral image is preprocessed by detecting target-like spectra and masking them from the subsequent endmember detection analysis. By partitioning target-like spectra from the scene, a set of spectral endmembers is detected which can be used to more accurately unmix the image. The vast majority of data in the original image can be interpreted in terms of these detected spectral endmembers. The few spectra which represent the bulk of the spectral diversity in the scene can then be interpreted individually.