Document Type





Principal component transform, Spectral Angle classification, Distributed algorithm, Performance prediction




Computer Engineering


This paper describes a novel distributed algorithm for use in remote-sensing, medical image analysis, and surveillance applications. The algorithm combines spectral-screening classification with the principal component transform (PCT), and human-centered mapping. It fuses a multi- or hyper-spectral image set into a single color composite image that maximizes the impact of spectral variation on the human visual system. The algorithm operates on distributed collections of shared-memory multiprocessors that are connected through high-performance networking. Scenes taken from a standard 210 frame remote-sensing data set, collected with the Hyper-spectral Digital Imagery Collection Experiment (HYDICE) airborne imaging spectrometer, are used to assess the algorithms image quality, performance, and scaling. The algorithm is supported with a predictive analytical model that allows its performance to be assessed for a wide variety of typical variations in use. For example, changes to the number of spectra, image resolution, processor speed, memory size, network bandwidth/latency, and granularity of decomposition. The motivation in building a performance model is to assess the impact of changes in technology and problem size associated with different applications, allowing cost-performance tradeoffs to be assessed.