  Hyperspectral image (HSI) classification has been widely adopted in
applications involving remote sensing imagery analysis which require high
classification accuracy and real-time processing speed. Methods based on
Convolutional neural networks (CNNs) have been proven to achieve
state-of-the-art accuracy in classifying HSIs. However, CNN models are often
too computationally intensive to achieve real-time response due to the high
dimensional nature of HSI, compared to traditional methods such as Support
Vector Machines (SVMs). Besides, previous CNN models used in HSI are not
specially designed for efficient implementation on embedded devices such as
FPGAs. This paper proposes a novel CNN-based algorithm for HSI classification
which takes into account hardware efficiency. A customized architecture which
enables the proposed algorithm to be mapped effectively onto FPGA resources is
then proposed to support real-time on-board classification with low power
consumption. Implementation results show that our proposed accelerator on a
Xilinx Zynq 706 FPGA board achieves more than 70x faster than an Intel 8-core
Xeon CPU and 3x faster than an NVIDIA GeForce 1080 GPU. Compared to previous
SVM-based FPGA accelerators, we achieve comparable processing speed but provide
a much higher classification accuracy.
