A Semi-Supervised Deep Learning Framework for Change Detection in Open-Pit Mines Using SAR Imagery
Detecting and monitoring changes in open-pit mines is crucial for efficient mining operations. Indeed, these changes comprise a broad spectrum of activities that can often lead to significant environmental impacts such as surface damage, air pollution, soil erosion, and ecosystem degradation. Conven...
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| Format: | Article |
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MDPI AG
2023-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/24/5664 |
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| author | Gianluca Murdaca Federico Ricciuti Alessio Rucci Bertrand Le Saux Alfio Fumagalli Claudio Prati |
| author_facet | Gianluca Murdaca Federico Ricciuti Alessio Rucci Bertrand Le Saux Alfio Fumagalli Claudio Prati |
| author_sort | Gianluca Murdaca |
| collection | DOAJ |
| description | Detecting and monitoring changes in open-pit mines is crucial for efficient mining operations. Indeed, these changes comprise a broad spectrum of activities that can often lead to significant environmental impacts such as surface damage, air pollution, soil erosion, and ecosystem degradation. Conventional optical sensors face limitations due to cloud cover, hindering accurate observation of the mining area. To overcome this challenge, synthetic aperture radar (SAR) images have emerged as a powerful solution, due to their unique ability to penetrate clouds and provide a clear view of the ground. The open-pit mine change detection task presents significant challenges, justifying the need for a model trained for this specific task. First, different mining areas frequently include various features, resulting in a diverse range of land cover types within a single scene. This heterogeneity complicates the detection and distinction of changes within open-pit mines. Second, pseudo changes, e.g., equipment movements or humidity fluctuations, which show statistically reliable reflectivity changes, lead to false positives, as they do not directly correspond to the actual changes of interest, i.e., blasting, collapsing, or waste pile operations. In this paper, to the best of our knowledge, we present the first deep learning model in the literature that can accurately detect changes within open-pit mines using SAR images (TerraSAR-X). We showcase the fundamental role of data augmentations and a coherence layer as a critical component in enhancing the model’s performance, which initially relied solely on amplitude information. In addition, we demonstrate how, in the presence of a few labels, a pseudo-labeling pipeline can improve the model robustness, without degrading the performance by introducing misclassification points related to pseudo changes. The F1-Score results show that our deep learning approach is a reliable and effective method for SAR change detection in the open-pit mining sector. |
| first_indexed | 2024-03-08T20:24:21Z |
| format | Article |
| id | doaj.art-a6b7371183e6442f86e9178328118bf6 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-08T20:24:21Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-a6b7371183e6442f86e9178328118bf62023-12-22T14:38:55ZengMDPI AGRemote Sensing2072-42922023-12-011524566410.3390/rs15245664A Semi-Supervised Deep Learning Framework for Change Detection in Open-Pit Mines Using SAR ImageryGianluca Murdaca0Federico Ricciuti1Alessio Rucci2Bertrand Le Saux3Alfio Fumagalli4Claudio Prati5Department of Electronics, Information and Bioengineering, Polytechnic University of Milan, 20133 Milan, ItalyTRE ALTAMIRA s.r.l., Ripa di Porta Ticinese, 79, 20143 Milan, ItalyTRE ALTAMIRA s.r.l., Ripa di Porta Ticinese, 79, 20143 Milan, ItalyEuropean Space Agency (ESA), <i>ϕ</i>-lab, 00044 Frascati, ItalyTRE ALTAMIRA s.r.l., Ripa di Porta Ticinese, 79, 20143 Milan, ItalyDepartment of Electronics, Information and Bioengineering, Polytechnic University of Milan, 20133 Milan, ItalyDetecting and monitoring changes in open-pit mines is crucial for efficient mining operations. Indeed, these changes comprise a broad spectrum of activities that can often lead to significant environmental impacts such as surface damage, air pollution, soil erosion, and ecosystem degradation. Conventional optical sensors face limitations due to cloud cover, hindering accurate observation of the mining area. To overcome this challenge, synthetic aperture radar (SAR) images have emerged as a powerful solution, due to their unique ability to penetrate clouds and provide a clear view of the ground. The open-pit mine change detection task presents significant challenges, justifying the need for a model trained for this specific task. First, different mining areas frequently include various features, resulting in a diverse range of land cover types within a single scene. This heterogeneity complicates the detection and distinction of changes within open-pit mines. Second, pseudo changes, e.g., equipment movements or humidity fluctuations, which show statistically reliable reflectivity changes, lead to false positives, as they do not directly correspond to the actual changes of interest, i.e., blasting, collapsing, or waste pile operations. In this paper, to the best of our knowledge, we present the first deep learning model in the literature that can accurately detect changes within open-pit mines using SAR images (TerraSAR-X). We showcase the fundamental role of data augmentations and a coherence layer as a critical component in enhancing the model’s performance, which initially relied solely on amplitude information. In addition, we demonstrate how, in the presence of a few labels, a pseudo-labeling pipeline can improve the model robustness, without degrading the performance by introducing misclassification points related to pseudo changes. The F1-Score results show that our deep learning approach is a reliable and effective method for SAR change detection in the open-pit mining sector.https://www.mdpi.com/2072-4292/15/24/5664deep learningchange detectionsynthetic aperture radar (SAR)mining |
| spellingShingle | Gianluca Murdaca Federico Ricciuti Alessio Rucci Bertrand Le Saux Alfio Fumagalli Claudio Prati A Semi-Supervised Deep Learning Framework for Change Detection in Open-Pit Mines Using SAR Imagery Remote Sensing deep learning change detection synthetic aperture radar (SAR) mining |
| title | A Semi-Supervised Deep Learning Framework for Change Detection in Open-Pit Mines Using SAR Imagery |
| title_full | A Semi-Supervised Deep Learning Framework for Change Detection in Open-Pit Mines Using SAR Imagery |
| title_fullStr | A Semi-Supervised Deep Learning Framework for Change Detection in Open-Pit Mines Using SAR Imagery |
| title_full_unstemmed | A Semi-Supervised Deep Learning Framework for Change Detection in Open-Pit Mines Using SAR Imagery |
| title_short | A Semi-Supervised Deep Learning Framework for Change Detection in Open-Pit Mines Using SAR Imagery |
| title_sort | semi supervised deep learning framework for change detection in open pit mines using sar imagery |
| topic | deep learning change detection synthetic aperture radar (SAR) mining |
| url | https://www.mdpi.com/2072-4292/15/24/5664 |
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