TY - GEN
T1 - Hyperspectral Video Processing on Resource-Constrained Platforms
AU - Li, Honglei
AU - Pan, Lei
AU - Lee, Eung Joo
AU - Li, Zhu
AU - Hoffman, Matthew J.
AU - Vodacek, Anthony
AU - Bhattacharyya, Shuvra S.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85077519122
UR - https://www.scopus.com/pages/publications/85077519122#tab=citedBy
U2 - 10.1109/WHISPERS.2019.8921138
DO - 10.1109/WHISPERS.2019.8921138
M3 - Conference contribution
AN - SCOPUS:85077519122
T3 - Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
BT - 2019 10th Workshop on Hyperspectral Imaging and Signal Processing
PB - IEEE Computer Society
T2 - 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2019
Y2 - 24 September 2019 through 26 September 2019
ER -