DeepDR: A Two-Level Deep Defect Recognition Framework for Meteorological Satellite Images
Raw meteorological satellite images often suffer from defects such as noise points and lines due to atmospheric interference and instrument errors. Current solutions typically rely on manual visual inspection to identify these defects. However, manual inspection is labor-intensive, lacks uniform sta...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/4/585 |
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| Summary: | Raw meteorological satellite images often suffer from defects such as noise points and lines due to atmospheric interference and instrument errors. Current solutions typically rely on manual visual inspection to identify these defects. However, manual inspection is labor-intensive, lacks uniform standards, and is prone to both false positives and missed detections. To address these challenges, we propose DeepDR, a two-level deep defect recognition framework for meteorological satellite images. DeepDR consists of two modules: a transformer-based noise image classification module for the first level and a noise region segmentation module based on a pseudo-label training strategy for the second level. This framework enables the automatic identification of defective cloud images and the detection of noise points and lines, thereby significantly improving the accuracy of defect recognition. To evaluate the effectiveness of DeepDR, we have collected and released two satellite cloud image datasets from the FengYun-1 satellite, which include noise points and lines. Subsequently, we conducted comprehensive experiments to demonstrate the superior performance of our approach in addressing the satellite cloud image defect recognition problem. |
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| ISSN: | 2072-4292 |