Cloud segmentation in satellite imagery using attention-based deep learning

Khola Naseem, Muhammad Imran Malik, Sheraz Ahmed

Abstract: Remote sensing imagery provides essential information for various applications. Automated detection of cloud-covered areas in satellite imagery is a crucial first step in many of these applications. Pixel-level identification of clouds in satellite images is a challenging task due to the diverse types of clouds and limited number of bands available in different satellite systems. Recently deep convolutional networks have been often used to achieve state-of-the-art performance in pixel-level object identification for remote sensing applications. In this paper, to address the challenging task of accurate semantic segmentation of clouds in multispectral satellite imagery, we propose an end-to-end attention-based deep convolutional neural network. The architecture of our proposed network comprises an encoder, an attention module, and a decoder. The attention-based model employs a ResNet encoder-decoder backbone, enhanced with residual connections, spatial pyramid pooling, and atrous convolutions. This combination enables the network to focus on more relevant features and improve cloud extraction accuracy. We evaluated our approach on two benchmark datasets from different satellite systems. Experimental results show that the proposed network achieves superior performance compared to existing advanced methods. across several segmentation metrics, including precision, recall, accuracy, and the Jaccard index.

External IDs:dblp:journals/mta/NaseemMA25