High efficiency on-board hyperspectral image classification with zynq SoC

Abstract

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.