In order to solve the problems of high algorithm complexity, long operation time and large amount of data calculation in the real-time image processing process of the gray level co-occurrence matrix for remote sensing cloud image feature extraction, a hardware acceleration method of Vivado HLS to realize the satellite remote sensing cloud image feature extraction algorithm is proposed. This paper studies the gray-level co-occurrence matrix texture feature extraction algorithm and Vivado HLS hardware acceleration design, uses Vivado HLS to hardware-accelerate the gray-level co-occurrence matrix texture special diagnosis extraction, and encapsulates it as a callable IP core to remotely sense cloud images on the PC side The processing results are compared with the processing results after Zynq7020 hardware acceleration. The experimental results show that this method can quickly resolve the texture characteristics of remote sensing cloud images, speed up the processing speed of remote sensing cloud images, and overcome the disadvantage of FPGA design of image algorithms.