Clouds Detection in Polar Icy Terrains: A Deformable Attention-Based Deep Neural Network for Multispectral Polar Scene Parsing

Cloud detection is crucial for remote sensing image analysis. Presently, deep learning methods are the preferred choice for cloud detection. Nevertheless, the prevalence of luminous ice and snow substrates in polar regions introduces significant complexities for cloud detection in medium-resolution remote sensing imagery. Consequently, conventional deep learning methods often misclassify clouds in intricate regions. Considering these challenges, we introduce a deep convolutional neural network model called DLACD-Net. This model is purpose-built for cloud detection and segmentation in medium-resolution, multispectral (VIS+SWIR) polar remote sensing images. DLACD-Net incorporates a nested U-shaped encoder–decoder structure and proposes DSACRSU at each encoding–decoding stage. These enhancements significantly improve the model's feature extraction capability. In addition, we propose the deformed large kernel attention into the decoding stage. It can capture finer details and boundary information of the target object, autonomously determining the level of attention required in different regions based on the task. To address class imbalance in training samples, we introduce $\alpha$-balanced focal loss, which focuses the model on challenging minority samples, leading to improved overall performance. Leveraging prior research, we adopt semisupervised learning techniques that combine visible light and short-wave infrared band data to construct the FY-3D MERSI-II dataset specifically for polar cloud detection. Within the dataset, our proposed model achieves an impressive overall classification accuracy of 95.97% and a mean intersection over union of 90.98%. Experimental results demonstrate that our proposed model outperforms baseline models primarily due to superior feature extraction. Furthermore, we systematically analyze the contribution of each added substructure.