IEEE Access (Jan 2024)

Cloud Detection Method Based on Improved DeeplabV3+ Remote Sensing Image

  • Jianguo Qian,
  • Jinlong Ci,
  • Hai Tan,
  • Wenwen Xu,
  • Yang Jiao,
  • Peiyou Chen

DOI
https://doi.org/10.1109/ACCESS.2024.3353205
Journal volume & issue
Vol. 12
pp. 9229 – 9242

Abstract

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Cloud cover is a phenomenon that inevitably exists in remote sensing images, and ground information is lost due to the presence of clouds. To a large extent, it causes degradation of the remote sensing image quality. Therefore, the detection of clouds in remote sensing images is the foundation and key to further emphasizing and use of remote sensing image information. To tackle the question of misjudgment, omission, and long training time of current deep learning-based cloud detection methods, this article suggests an improved cloud detection method for remote sensing images with a deeplabV3+ network. The method aims to detect cloud-covered regions in remote sensing images more accurately, quickly and efficiently. The training time is reduced by improving the Xception backbone network to reduce the amount of parameters; the improved CBAM module is added after the Atrous Spatial Pyramid Pooling (ASPP) module to strengthen the sensory field to better capture the contextual information; and the GAU module is used to replace the traditional bilinear interpolation up-sampling in the decoder part, which results in a better quality of the up-sampling, more spatial sense of the fusion, and improved accuracy of the segmentation. Experiments were conducted by using datasets from the homegrown ZY-03 satellite and comparing them with traditional DeeplabV3+, MobileNet and U-Net. And the generalization ability is verified using the public Sentinal-2 dataset. Compared to legacy deeplabV3+ networks, the accuracy rate is improved by 3.91%, the improved precision by 3.12%, the improved recall by 2.78%, and the improved Mean Intersection over Union (MIoU) by 5.75%. Compared to Mobilenet, the improved accuracy by 1.79%, the improved precision by 1.47%, the improved recall by 1.42%, and the improved Mean Intersection over Union (MIoU) by 4.15%. Compared with the Unet network, the improved accuracy by 1.28%, the improved precision by 1.36%, the improved recall by 1.32%, and the improved Mean Intersection over Union (MIoU) by 3.36%. The method presented in this article demonstrates superior performance in cloud detection compared to several other networks.

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