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Hyper-spectral Image Processing Applications on the SIMD Pixel Processor for the Digital Battlefield
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BibTeX
@MISC{Chai_hyper-spectralimage,
author = {Sek M. Chai and Antonio Gentile and Wilfredo E. Lugo-beauchamp and José L. Cruz-rivera and D. Scott Wills},
title = {Hyper-spectral Image Processing Applications on the SIMD Pixel Processor for the Digital Battlefield},
year = {}
}
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Abstract
Future military scenarios will rely on advanced imaging sensor technology beyond the visible spectrum to gain total battlefield awareness. Real-time processing of these data streams requires tremendous computational workloads and I/O throughputs. This paper presents three applications for hyper-spectral data streams, vector quantization, region autofocus, and K-means clustering, on the SIMD Pixel Processor (SIMPil). In SIMPil, an image sensor array (focal plane) is integrated on top of a SIMD computing layer to provide direct coupling between sensors and processors, alleviating I/O bandwidth bottlenecks while maintaining low power consumption and portability. Simulation results with sustained operation throughputs of 500-1500 Gops/sec support real-time performances and promote focal plane processing on SIMPil. Keywords: Military applications, Hyper-spectral imaging, Object detection and tracking, Segmentation,
Keyphrases
simd pixel processor hyper-spectral image processing application digital battlefield total battlefield awareness focal plane advanced imaging sensor technology military application real-time processing object detection hyper-spectral imaging tremendous computational workload future military scenario image sensor array hyper-spectral data stream low power consumption k-means clustering focal plane processing sustained operation throughput region autofocus simulation result vector quantization data stream visible spectrum
