Impact of Vector Ordering Strategies on Morphological Unmixing of Remotely Sensed Hyperspectral Images
Hyper spectral imaging is a new technique in remote sensing that generates hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. In previous work, we have explored the application of morphological operations to integrate both spatial and spectral responses in hyper spectral data analysis. These operations rely on ordering pixel vectors in spectral space, but there is no unambiguous means of defining the minimum and maximum values between two vectors of more than one dimension. Our original contribution in this paper is to examine the impact of different vector ordering strategies on the definition of multi-channel morphological operations. Our focus is on morphological unmixing, which decomposes each pixel vector in the hyper spectral scene into a combination of pure spectral signatures (called end members) and their associated abundance fractions, allowing sub-pixel characterization. Experiments are conducted using real hyper spectral data sets collected by NASA/JPL's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) system.