MCNet: Multi-Scale Feature Extraction and Content-Aware Reassembly Cloud Detection Model for Remote Sensing Images
Cloud detection plays a vital role in remote sensing data preprocessing. Traditional cloud detection algorithms have difficulties in feature extraction and thus produce a poor detection result when processing remote sensing images with uneven cloud distribution and complex surface background. To ach...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/13/1/28 |
| Summary: | Cloud detection plays a vital role in remote sensing data preprocessing. Traditional cloud detection algorithms have difficulties in feature extraction and thus produce a poor detection result when processing remote sensing images with uneven cloud distribution and complex surface background. To achieve better detection results, a cloud detection method with multi-scale feature extraction and content-aware reassembly network (MCNet) is proposed. Using pyramid convolution and channel attention mechanisms to enhance the model’s feature extraction capability, MCNet can fully extract the spatial information and channel information of clouds in an image. The content-aware reassembly is used to ensure that sampling on the network can recover enough in-depth semantic information and improve the model cloud detection effect. The experimental results show that the proposed MCNet model has achieved good detection results in cloud detection tasks. |
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| ISSN: | 2073-8994 |