Parallel design of convolutional neural networks for remote sensing images object recognition based on data-driven array processor

Object recognition in very high-resolution remote sensing images is a basic problem in the field of aerial and satellite image analysis. With the development of sensor technology and aerospace remote sensing technology, the quality and quantity of remote sensing images are improved. Traditional recognition methods have a certain limitation in describing higher-level features, but object recognition method based on convolutional neural network(CNN) can not only deal with large scale images, but also train features automatically with high efficiency. It is mainly used on object recognition for remote sensing images. In this paper, an AlexNet CNN model is trained using 2 100 remote sensing images, and correction rate can reach 97.6% after 2 000 iterations. Then based on trained model, a parallel design of CNN for remote sensing images object recognition based on data-driven array processor(DDAP) is proposed. The consuming cycles are counted. Simultaneously, the proposed architecture is realized on Xilinx V6 development board, and synthesized based on SMIC 130 nm complementary metal oxid semiconductor(CMOS) technology. The experimental results show that the proposed architecture has a certain degree of parallelism to achieve the purpose of accelerating calculations.