Hyperspectral image classification for remote sensing using low-power neuromorphic hardware
2019 International Joint Conference on Neural Networks (IJCNN), 2019•ieeexplore.ieee.org
In this paper, we present a novel feature extraction algorithm based approach for performing
Hyperspectral Image Classification using a low-power Neuromorphic hardware. The
application of interest for this study is HSI image classification for remote sensing. We
demonstrate energy-efficient data processing pipeline optimized to use with on-edge
neuromorphic hardware. The dataset used for the study is Salinas-A. We use the Brilliant
USB stick with 4 NM500 chips for prototyping the application. Achieved recognition time is …
Hyperspectral Image Classification using a low-power Neuromorphic hardware. The
application of interest for this study is HSI image classification for remote sensing. We
demonstrate energy-efficient data processing pipeline optimized to use with on-edge
neuromorphic hardware. The dataset used for the study is Salinas-A. We use the Brilliant
USB stick with 4 NM500 chips for prototyping the application. Achieved recognition time is …
In this paper, we present a novel feature extraction algorithm based approach for performing Hyperspectral Image Classification using a low-power Neuromorphic hardware. The application of interest for this study is HSI image classification for remote sensing. We demonstrate energy-efficient data processing pipeline optimized to use with on-edge neuromorphic hardware. The dataset used for the study is Salinas-A. We use the Brilliant USB stick with 4 NM500 chips for prototyping the application. Achieved recognition time is 18.4 μs and energy consumption is ~10 μJ with an accuracy of ~ 97%.
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