Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 19, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2021.3110869
Keywords
Feature extraction; Decoding; Fuses; Cloud computing; Image reconstruction; Convolution; Visualization; Boundary points; cloud detection; feature fusion
Categories
Funding
- National Key Research and Development Program of China [2019YFC1510905]
- National Natural Science Foundation of China [62125102]
- Beijing Natural Science Foundation [4192034]
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The proposed cloud detection network, ABNet, includes All-scale feature Fusion modules and a Boundary point Prediction module, which can optimize features, recover spatial information, and improve accuracy near cloud boundaries.
Cloud detection is a significant pre-processing for remote sensing images. In recent years, many methods based on deep learning are proposed to detect clouds and multi-scale feature fusion is often used in these methods. However, most existing methods fuse features through concatenation and element-wise summation, which are simple and can be improved in spatial information recovery. Therefore, we explore the way of fusing features to recover the missing spatial information more sufficiently. Besides, we also observe that some cloud detection results are not accurate enough near the boundary of clouds. In view of the above observations, in this letter, we propose a cloud detection network, ABNet, which includes All-scale feature Fusion modules and a Boundary point Prediction module. The All-scale feature Fusion module can optimize the features and recover spatial information by integrating features of all scales. And the Boundary point Prediction module further remedies cloud boundary information by classifying the cloud boundary points separately. Experimental results demonstrate that our method improves the accuracy of cloud detection compared with other methods.
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