A new approach to mixed pixel classification of hyperspectral imagery based on extended morphological profiles

This paper presents a new approach to the analysis of hyperspectral images, a new class of image data that is mainly used in remote sensing applications. The method is based on the generalization of concepts from mathematical morphology to multi-channel imagery. A new vector organization scheme is described, and fundamental morphological vector operations are defined by extension. Theoretical definitions of extended morphological operations are used in the formal definition of the concept of extended morphological profile, which is used for multi-scale analysis of hyperspectral data. This approach is particularly well Suited for the analysis of image scenes where most of the pixels collected by the sensor are characterized by their mixed nature, i.e. they are formed by a combination Of multiple underlying responses produced by spectrally distinct materials. Experimental results demonstrate the applicability of the proposed technique in mixed pixel analysis of simulated and real hyperspectral data collected by the NASA/Jet Propulsion Laboratory Airborne Visible/Infrared Imaging Spectrometer and the DLR Digital Airborne (DAIS 7915) and Reflective Optics System Imaging Spectrometers. The proposed method works effectively in the presence of noise and low spatial resolution. A quantitative and comparative performance Study with regards to other standard hyperspectral analysis methodologies reveals that the combined utilization of spatial and spectral information in the proposed technique produces classification results which are superior to those found by using the spectral information alone. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.