ABSTRACT
Improvement of spatial and spectral resolution in latest-generation Earth observation instruments is introducing extremely high computational requirements in many remote sensing applications. While thematic classification applications have greatly benefited from this increasing amount of information, new computational requirements have been introduced, in particular, for hyperspectral image data sets with
hundreds of spectral channels and very fine spatial resolution. Low-cost parallel computing architectures such as heterogeneous networks of computers have quickly become a standard tool of choice for dealing with the massive amount of image data sets. In this chapter, a new parallel classification algorithm for hyperspectral imagery based on morphological neural networks is presented and discussed. The parallel algorithm is mapped onto heterogeneous and homogeneous parallel platforms using a hybrid partitioning scheme. In order to test the accuracy and parallel performance of the proposed approach, we have used two networks of workstations distributed among different locations, and also a massively parallel Beowulf cluster at NASA’s Goddard Space Flight Center in Maryland. Experimental results are provided in the context of a real agriculture and farming application, using hyperspectral data acquired by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRS), operated by the NASA Jet Propulstion Laboratory, over the valley of Salinas in California.
