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As one kind of remote sensing image (RSI), Directional Polarimetric Camera (DPC) data are of great significance in atmospheric radiation transfer and climate feedback. The availability of DPC images is often hindered by clouds, and effective cloud detection is the premise of many applications. Conventional threshold-based cloud detection methods are limited in performance and generalization capability. In this paper, we propose an effective learning-based 3D multimodal fusion cloud detection network (MFCD-Net) model. The network is a three-input stream architecture with a 3D-Unet-like encoder-decoder structure to fuse the multiple modalities of reflectance image, polarization image Q, and polarization image U in DPC imagery, with consideration of the angle and spectral information. Furthermore, cross attention is utilized in fusing the polarization features into the spatial-angle-spectral features in the reflectance image to enhance the expression of the fused features. The dataset used in this paper is obtained from the DPC cloud product and the cloud mask product. The proposed MFCD-Net achieved excellent cloud detection performance, with a recognition accuracy of 95.74%, according to the results of the experiments. [ABSTRACT FROM AUTHOR]

Cloud detection is one of the critical tasks in remote sensing image preprocessing. Remote sensing images usually contain multi-dimensional information, which is not utilized entirely in existing deep learning methods. This paper proposes a novel cloud detection algorithm based on multi-scale input and dual-channel attention mechanisms. Firstly, we remodeled the original data to a multi-scale layout in terms of channels and bands. Then, we introduced the dual-channel attention mechanism into the existing semantic segmentation network, to focus on both band information and angle information based on the reconstructed multi-scale data. Finally, a multi-scale fusion strategy was introduced to combine band information and angle information simultaneously. Overall, in the experiments undertaken in this paper, the proposed method achieved a pixel accuracy of 92.66% and a category pixel accuracy of 92.51%. For cloud detection, the proposed method achieved a recall of 97.76% and an F1 of 95.06%. The intersection over union (IoU) of the proposed method was 89.63%. Both in terms of quantitative results and visual effects, the deep learning model we propose is superior to the existing semantic segmentation methods. [ABSTRACT FROM AUTHOR]