Description

Hyperspectral imaging offers valuable spectral diversity for scene analysis and information extraction. However, exploiting this spectral diversity involves significant challenges in performing efficient video processing, especially in resource-constrained environments. These challenges arise due to the high memory and computational requirements for hyperspectral video processing applications. This paper presents system design methods using band subset selection to address this problem. These methods are applied to develop an adaptive video processing system targeted to an Android platform. The system dynamically adapts the selected bands to process based on constraints on real-time performance and video analysis accuracy. Experimental results provide quantitative insight into trade-offs between accuracy and real-time performance under stringent resource constraints. The results also validate the effectiveness of the proposed system in performing adaptive, resource-constrained hyperspectral video processing.

Date of creation, presentation, or exhibit

9-24-2019

Comments

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Document Type

Conference Proceeding

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)

Campus

RIT – Main Campus

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