The effects of misregistration between hyperspectral and panchromatic images on linear spectral unmixing

Abstract

ABSTRACT Low probability subpixel target extraction and identification is important for hyperspectral (HS) applications. To better exploit the signatures of targets and extract targets from mixed pixels, we proposed a linear spectral unmixing algorithm combining HS and panchromatic (Pan) images (called SU-Co-Ims). However, misregistration between HS and Pan images is common, which usually has a negative impact on subsequent applications. Our motivation is to attempt to quantify the misregistration error range in which the extracted subpixel target is valid. To determine the maximum acceptable misregistration error (MAME), we focus on analysing the impact of misregistration between images on target extraction, that is, the effects of misregistration between images on linear spectral unmixing. Taking Pan image as reference, HS image and Pan image are intentionally misregistered in the along-track direction. The proposed SU-Co-Ims method is applied to decompose mixed pixels and extract subpixel targets. Spectral angle mapper (SAM) and Euclidean distance (ED) are used to evaluate spectral unmixing error introduced by misregistration between images. Results indicate that spectral unmixing error increases with misregistration error, and the MAME varies from 0.35 to 1.94 pixels for imagers with different spatial resolution. Consequently, accurate image registration remains crucial to unmixing-based subpixel target extraction, but misregistration has a low impact on results when the misregistration error between images is smaller than the MAME.