MATEC Web of Conferences
Volume 45, 20162016 7th International Conference on Mechatronics and Manufacturing (ICMM 2016)
|Number of page(s)||6|
|Section||Computer aided manufacturing technology|
|Published online||15 March 2016|
High Efficiency On-Board Hyperspectral Image Classification with Zynq SoC
Department of Automatic Test and Control, Harbin Institute of Technology, Harbin, China
a Corresponding author: firstname.lastname@example.org
Because of the downlink bandwidth bottleneck and power limitation on satellite, the demands for low power cost high performance on-board payload data processing which can reduce the volume of communication data are growing as well. This paper propos es a high efficiency architecture for on-board hyperspectral image classification in a Zynq Soc to achieve real-time performance. The Hamming-distance based Support vector machine (SVM) is adopted to get a high accuracy and low energy consumption for multi-class classification. The sequential control and the computing data path are realized in ARM processor and Programmable logic respectively. By the pipelined computing data path, a satisfying speedup is reached and thus lowers the energy consumption. The experiments on real hyperspectral image datasets demonstrate that our architecture can achieve 97.8% overall accuracy, 2.5~330x speed up and 11~835x energy saving compared with different state-of-art embedded platforms. For the AVIRIS spectrometer in real NASA application, it can realize real-time image classification.
© Owned by the authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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