An SVM-based Hardware Accelerator for Onboard Classification of Hyperspectral Images

Abstract

Hyperspectral images (HSIs) have been used in civil and military scenarios for ground recognition, urban development management, rare minerals identification, and diverse other purposes. However, HSIs have a significant volume of information and require high computational power, especially for real-time processing in embedded applications, as in onboard computers in satellites. These issues have driven the development of hardware-based solutions able to provide the processing power necessary to meet such requirements. In this paper, we present a hardware accelerator to enhance the performance of one of the most computational expensive stages of HSI processing: the classification. We have employed the Entropy Multiple Correlation Ratio procedure to select the spectral bands to be used in the training process. For the classification step, we have applied a Support Vector Machine classifier with a Hamming Distance decision approach. The proposed custom processor was implemented in FPGA and compared with high-level implementations. The results obtained demonstrate that the processor has a silicon cost lower than similar solutions and can perform a realtime pixel classification in 0.1 ms and achieves a state-of-the-art accuracy of 99.7%.