Leveraging Frequency-Guided Mixer and Target-Aware Attention for Ground-Based Cloud Detection

Compared to satellite imagery, ground-based cameras capture cloud data (ground-to-sky data) with higher temporal and spatial resolutions, providing more detailed cloud information. However, the spectral information available in ground-to-sky data is limited. Therefore, extracting features with strong discrimination from optical remote sensing images (ORSIs) is challenging. Currently, deep-learning-based cloud detection methods face two main challenges. First, although convolutional neural networks (CNNs) effectively extract high-frequency (HF) components from images through convolutions, they struggle to capture low-frequency (LF) components, which are capable of representing global features and target structures. Second, in ORSIs, the spectral characteristics of thin clouds and the sky are similar, making it difficult to distinguish cloud regions from the background. To address these challenges, we propose a network consisting of two main modules: the mixer module (MM) and the cloud-aware attention module (CAAM). The MM comprises an HF and an LF components extraction branch. The HF branch extracts local textures through max-pooling and parallel convolution operations. The LF branch captures long-range dependency by decomposing a large kernel convolution. It leverages the advantages of both convolution and self-attention to effectively capture global features. In addition, we introduce the CAAM, which quantifies images into histograms to separate clouds from the background and enhances the perception of clouds using an attention mechanism. We conducted experiments using both daytime and nighttime cloud image data from the SWINySeg dataset with mIoU reaching 88.93% and overall accuracy (OA) reaching 93.97%. The results demonstrate that our proposed method achieves promising performance compared to state-of-the-art cloud detection methods.