FPGA based implementation of convolutional neural network for hyperspectral classification

convolutional neural network (CNN) has been widely used for hyperspectral classification. Current researches of CNN based hyperspectral image classification is mainly implemented on graphics processing unit (GPU) platform. However, GPU is not suitable for onboard processing due to the problem of space radiation and power supply on image acquiring platform. Therefore, in this paper, FPGA is selected to implement CNN based hyperspectral classification for further onboard processing. Specially, a hardware model is designed for the forward classification step of CNN using hardware description language, including computation structure for CNN, implementation of different layers, weight loading scheme, and data interfere. Simulation results over Pavia data set validate the proposed FPGA based implementation is coincide with that on GPU platform.