Multi-level Information Fusion Network with Edge Information Injection for Single-band Cloud Detection

Current cloud detection methods have demonstrated effectiveness by utilizing the rich spectral features of multi-spectral images. Compared to multispectral images, single-band infrared images offer higher efficiency in terms of sampling and processing speed. However, single-band cloud detection methods have not been fully developed, and existing methods based on multispectral cloud detection have some limitations when applied directly to single-band images: Firstly, they often blend shallow features containing spatial details with deep features providing high-level semantic information, yet struggle to disentangle features with strong discrimination representing cloud edges and bodies from limited information. Additionally, the correlation between features at different aspects is not fully reasoned, resulting in blurred boundary segmentation. To address these issues, we introduce a Multi-level Information Fusion Network (MIFNet) with an integrated edge information injection strategy. Our method effectively decouples clouds into their fundamental components: body and edge (Low-Frequency (LF) and High-Frequency (HF) components), enabling the comprehensive acquisition of strong discriminative features. Specifically, we propose an Edge Feature Extraction Module (EFEM) that isolates the cloud body through low-pass filtering, while the cloud’s edge is extracted by subtracting lower-level features from LF components. Furthermore, we employ a Feature Refinement Module (FRM) to locate the cloud body’s position precisely. Building upon this foundation, we devise a Graph Reasoning Module (GRM) to facilitate the full inference of feature correlations at different levels and to model the global interdependence between edges and semantics. Through comprehensive evaluations on benchmark datasets comprising infrared band images from Landsat 8 and MODIS satellites, we demonstrate that our proposed MIFNet outperforms state-of-the-art methods, yielding promising results in cloud detection accuracy. Our code is publicly available at https://github.com/KwunYat/MIFNet.