Impact of Vector Ordering Strategies on Morphological Unmixing of Remotely Sensed Hyperspectral Images

Hyperspectral 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 hyperspectral 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 hyperspectral scene into a combination of pure spectral signatures (called endmembers) and their associated abundance fractions, allowing sub-pixel characterization. Experiments are conducted using real hypers...

• Type: Conference
• Year: 2010
• Where: ICPR
• Authors: Antonio Plaza, Javier Plaza
• Keywords: Computer Vision, Hyperspectral, Hyperspectral Data, ICPR 2010, Morphological Operations