Hardware Implementation of Convolutional Neural Network-Based Remote Sensing Image Classification Method

The convolutional neural networks have achieved very good results in the field of remote sensing image classification and recognition. However, the cost of huge computational complexity with the significant accuracy improvement of CNNs makes a huge challenge to hardware implementation. A promising solution is FPGA due to it supports parallel computing with low power consumption. In this paper, LeNet-5-based remote sensing image classification method is implemented on FPGA. The test images with a size of 126 x 126 are transformed to the system from PC by serial port. The classification accuracy is 98.18% tested on the designed system, which is the same as that on PC. In the term of efficiency, the designed system runs 2.29 ms per image, which satisfies the real-time requirements.