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On the use of parallel computing to process multichannel imagery via extended morphological operations

Multichannel images are characteristic of certain applications, such as medical imaging or remotely sensed data analysis. In such images, each pixel is given by a vector of values. Due to the large data volumes often associated with multichannel imagery, there is a need for parallel algorithms able to process those data quickly enough for practical use. This paper describes a parallel implementation of a multichannel image processing algorithm that naturally combines spatial and spectral/temporal information. The algorithm combines a vector-preserving approach to extend morphological operations to multichannel images with the watershed transformation used in morphological processing, It is evaluated using remotely sensed hyperspectral data collected by the NASA’s Jet Propulsion Laboratory Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS).