Using hyperspectral unmixing for the analysis of very high spatial resolution hyperspectral imagery

Abstract

Traditional hyperspectral unmixing is focused on subpixel material composition extraction for low and moderate resolution imagery. Technological advances are making affordable hyperspectral imagers that can be used for very high spatial resolution imaging in many applications. A question that we want to address in this work is whether a traditional hyperspectral image analysis technique like unmixing still has value in the context of very high spatial resolution hyperspectral imaging (VHSR-HSI). In this paper, we will present preliminary results on how unsupervised hyperspectral unmixing algorithms can be used to extract spectral signatures of materials in a VHSR-HSI to map their spatial distribution and capture their spectral variability. Examples using hyperspectral images collected at close range using a standoff hyperspectral imager and an unmanned airborne system are used to illustrate our approach.