Publications

Zhang, JQ; Lei, J; Xie, WY; Jiang, K; Zhang, X; Cao, MX; Li, YS (2024). Distribution-Aware Interactive Attention Network and Large-Scale Cloud Recognition Benchmark on FY-4A Satellite Image. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 62, 5408515.

Abstract
Accurate cloud recognition and warning are crucial for various applications, including in-flight support, weather forecasting, and climate research. However, recent deep learning algorithms have predominantly focused on detecting cloud regions in satellite imagery, with insufficient attention to the specificity required for accurate cloud recognition. This limitation inspired us to develop the novel FY-4A-Himawari-8 (FYH) dataset, which includes nine distinct cloud categories and uses precise domain adaptation methods to align 70419 image-label pairs (including 110000 train/5500 test $100\times 100$ size images) in terms of projection, temporal resolution, and spatial resolution, thereby facilitating the training of supervised deep learning networks. Given the complexity and diversity of cloud formations, we have thoroughly analyzed the challenges inherent to cloud recognition tasks, examining the intricate characteristics and distribution of the data. To effectively address these challenges, we designed a distribution-aware interactive-attention network (DIAnet), which preserves pixel-level details through a high-resolution branch and a parallel multiresolution cross-branch. We also integrated a distribution-aware loss (DAL) to mitigate the imbalance across cloud categories. An interactive attention module (IAM) further enhances the robustness of feature extraction combined with spatial and channel information. Empirical evaluations on the FYH dataset demonstrate that our method outperforms other cloud recognition networks, achieving superior performance in terms of mean intersection over union (mIoU). The code for implementing DIAnet is available at https://github.com/icey-zhang/DIAnet.

DOI:
10.1109/TGRS.2024.3453376

ISSN:
1558-0644