A Cloud Detection Method for Landsat 8 Satellite Remote Sensing Images Based on Improved CDNet Model

Abstract

Cloud detection in remote sensing images is a crucial task in various applications, such as meteorological disaster prediction and earth resource exploration, which require accurate cloud identification. This work proposes a cloud detection model based on the Cloud Detection neural Network (CDNet), incorporating a fusion mechanism of channel and spatial attention. Depthwise separable convolution is adopted to achieve a lightweight network model and enhance the efficiency of network training and detection. In addition, the Convolutional Block Attention Module (CBAM) is integrated into the network to train the cloud detection model with attention features in channel and spatial dimensions. Experiments were conducted on Landsat 8 imagery to validate the proposed improved CDNet. Averaged over all testing images, the overall accuracy (OA), mean Pixel Accuracy (mPA), Kappa coefficient and Mean Intersection over Union (MIoU) of improved CDNet were 96.38%, 81.18%, 96.05%, and 84.69%, respectively. Those results were better than the original CDNet and DeeplabV3+. Experiment results show that the improved CDNet is effective and robust for cloud detection in remote sensing images.