Distribution-aware Interactive Attention Network and Large-scale Cloud
  Recognition Benchmark on FY-4A Satellite Image
Jiaqing Zhang Jie Lei Weiying Xie Kai Jiang Mingxiang Cao Yunsong Li
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 70,419 image-label pairs 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 multi-resolution 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.