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Deep Convolutional Neural Network (DCNN)-based image scene classification models play an important role in a wide variety of remote sensing applications and achieve great success. However, the large-scale remote sensing images and the intensive computations make the deployment of these DCNN-based models on low-power processing systems (e.g., spaceborne or airborne) a challenging problem. To solve this problem, this paper proposes a high-performance Field-Programmable Gate Array (FPGA)-based DCNN accelerator by combining an efficient network compression scheme and reasonable hardware architecture. Firstly, this paper applies the network quantization to a high-accuracy remote sensing scene classification network, an improved oriented response network (IORN). The volume of the parameters and feature maps in the network is greatly reduced. Secondly, an efficient hardware architecture for network implementation is proposed. The architecture employs dual-channel Double Data Rate Synchronous Dynamic Random-Access Memory (DDR) access mode, rational on-chip data processing scheme and efficient processing engine design. Finally, we implement the quantized IORN (Q-IORN) with the proposed architecture on a Xilinx VC709 development board. The experimental results show that the proposed accelerator has 88.31% top-1 classification accuracy and achieves a throughput of 209.60 Giga-Operations Per Second (GOP/s) with a 6.32 W on-chip power consumption at 200 MHz. The comparison results with off-the-shelf devices and recent state-of-the-art implementations illustrate that the proposed accelerator has obvious advantages in terms of energy efficiency.

An Efficient FPGA-Based Implementation for Quantized Remote Sensing Image Scene Classification Network