Abstract
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, 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 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
