Research on fast cloud detection based on FPGA

Xiaokang Xu; Hongyang Bai; Yuxin Zhou; Hongwei Guo

doi:10.1117/12.2586203

12 March 2021 Research on fast cloud detection based on FPGA

Xiaokang Xu, Hongyang Bai, Yuxin Zhou, Hongwei Guo

Proceedings Volume 11763, Seventh Symposium on Novel Photoelectronic Detection Technology and Applications; 1176313 (2021) https://doi.org/10.1117/12.2586203
Event: Seventh Symposium on Novel Photoelectronic Detection Technology and Application 2020, 2020, Kunming, China

Abstract

Aiming at the feature that the cloud detection algorithm using cloud area features can take parallel computing at many places, combined with the advantage of pipeline parallel processing of FPGA processing data, this paper proposes a method for achieving high-speed real-time cloud detection based on FPGA development on the remote sensing camera side. And it designs logical architecture for the parallel computing and extraction of multiple cloud area features in the cloud detection algorithm. In addition, a method for solving the difference and comparison probability of image entropy is developed, which can achieve the rapid and accurate detection of cloud layers in remote sensing images. The hardware test results show that the proposed method can realize cloud detection processing on remote sensing images with an input resolution of 512*512 on the Xilinx z7020clg400 series FPGA platform, at the 12th pixel clock cycle after image transmission (the pixel clock is: 65MHz) to obtain the discrimination result and the accuracy of cloud area discrimination for the Landsat8 remote sensing atlas can reach 85.38%. Besides, the processing efficiency is greatly improved, which can meet the real-time cloud discrimination requirements of the spaceborne platform.

Citation Download Citation

Xiaokang Xu, Hongyang Bai, Yuxin Zhou, and Hongwei Guo "Research on fast cloud detection based on FPGA", Proc. SPIE 11763, Seventh Symposium on Novel Photoelectronic Detection Technology and Applications, 1176313 (12 March 2021); https://doi.org/10.1117/12.2586203

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