Design Issues for Hardware Implementation of an Algorithm for SegmentingHyperspectral Imagery

摘要

Modern hyperspectral imagers can produce data cubes with hundreds of spectralchannels and millions of pixels. One way to cope with this massive volume is to organize the data so that pixels with similar spectral content are clustered together in the same category. This provides both a compression of the data and a segmentation of the image that can be useful for other image processing tasks downstream. The classic approach for segmentation of multidimensional data is the k-means algorithm; this is an iterative method that produces successively better segmentations. It is a simple algorithm, but the computational expense can be considerable, particularly for clustering large hyperspectral images into many categories. The ASAPP (Accelerating Segmentation And Pixel Purity) project aims to relieve this processing bottleneck by putting the k-means algorithm into field-programmable gate array (FPGA) hardware. The standard software implementation of k-means uses floating-point arithmetic and Euclidean distances. By fixing the precision of the computation and by employing alternative distance metrics (we consider the 'Manhattan' and the 'Max' metrics as well as a linear combination of the two), we can fit more distance-computation nodes on the chip, obtain a higher degree of fine-grain parallelism, and therefore faster performance, but at the price of slightly less optimal clusters. We investigate the effects of different distance metrics from both a theoretical (using random simulated data) and an empirical viewpoint (using 224-channel AVIRIS images and 10-channel multispectral images that are derived from the AVIRIS data to simulate MTI data).