Techniques for the Automatic Detection and Hiding of Sensitive Targets in Emergency Mapping Based on Remote Sensing Data
Emergency remote sensing mapping can provide support for decision making in disaster assessment or disaster relief, and therefore plays an important role in disaster response. Traditional emergency remote sensing mapping methods use decryption algorithms based on manual retrieval and image editing t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/10/2/68 |
| _version_ | 1797412397493780480 |
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| author | Tianqi Qiu Xiaojin Liang Qingyun Du Fu Ren Pengjie Lu Chao Wu |
| author_facet | Tianqi Qiu Xiaojin Liang Qingyun Du Fu Ren Pengjie Lu Chao Wu |
| author_sort | Tianqi Qiu |
| collection | DOAJ |
| description | Emergency remote sensing mapping can provide support for decision making in disaster assessment or disaster relief, and therefore plays an important role in disaster response. Traditional emergency remote sensing mapping methods use decryption algorithms based on manual retrieval and image editing tools when processing sensitive targets. Although these traditional methods can achieve target recognition, they are inefficient and cannot meet the high time efficiency requirements of disaster relief. In this paper, we combined an object detection model with a generative adversarial network model to build a two-stage deep learning model for sensitive target detection and hiding in remote sensing images, and we verified the model performance on the aircraft object processing problem in remote sensing mapping. To improve the experimental protocol, we introduced a modification to the reconstruction loss function, candidate frame optimization in the region proposal network, the PointRend algorithm, and a modified attention mechanism based on the characteristics of aircraft objects. Experiments revealed that our method is more efficient than traditional manual processing; the precision is 94.87%, the recall is 84.75% higher than that of the original mask R-CNN model, and the F1-score is 44% higher than that of the original model. In addition, our method can quickly and intelligently detect and hide sensitive targets in remote sensing images, thereby shortening the time needed for emergency mapping. |
| first_indexed | 2024-03-09T05:01:28Z |
| format | Article |
| id | doaj.art-cc11e2ee26b54099816a2f8586f49dc0 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-09T05:01:28Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-cc11e2ee26b54099816a2f8586f49dc02023-12-03T12:59:48ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-02-011026810.3390/ijgi10020068Techniques for the Automatic Detection and Hiding of Sensitive Targets in Emergency Mapping Based on Remote Sensing DataTianqi Qiu0Xiaojin Liang1Qingyun Du2Fu Ren3Pengjie Lu4Chao Wu5School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaSchool of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaEmergency remote sensing mapping can provide support for decision making in disaster assessment or disaster relief, and therefore plays an important role in disaster response. Traditional emergency remote sensing mapping methods use decryption algorithms based on manual retrieval and image editing tools when processing sensitive targets. Although these traditional methods can achieve target recognition, they are inefficient and cannot meet the high time efficiency requirements of disaster relief. In this paper, we combined an object detection model with a generative adversarial network model to build a two-stage deep learning model for sensitive target detection and hiding in remote sensing images, and we verified the model performance on the aircraft object processing problem in remote sensing mapping. To improve the experimental protocol, we introduced a modification to the reconstruction loss function, candidate frame optimization in the region proposal network, the PointRend algorithm, and a modified attention mechanism based on the characteristics of aircraft objects. Experiments revealed that our method is more efficient than traditional manual processing; the precision is 94.87%, the recall is 84.75% higher than that of the original mask R-CNN model, and the F1-score is 44% higher than that of the original model. In addition, our method can quickly and intelligently detect and hide sensitive targets in remote sensing images, thereby shortening the time needed for emergency mapping.https://www.mdpi.com/2220-9964/10/2/68emergency mapping based on remote sensing datasensitive object detectionsensitive object hidingmask R-CNN modelPointRendDeepfill model |
| spellingShingle | Tianqi Qiu Xiaojin Liang Qingyun Du Fu Ren Pengjie Lu Chao Wu Techniques for the Automatic Detection and Hiding of Sensitive Targets in Emergency Mapping Based on Remote Sensing Data ISPRS International Journal of Geo-Information emergency mapping based on remote sensing data sensitive object detection sensitive object hiding mask R-CNN model PointRend Deepfill model |
| title | Techniques for the Automatic Detection and Hiding of Sensitive Targets in Emergency Mapping Based on Remote Sensing Data |
| title_full | Techniques for the Automatic Detection and Hiding of Sensitive Targets in Emergency Mapping Based on Remote Sensing Data |
| title_fullStr | Techniques for the Automatic Detection and Hiding of Sensitive Targets in Emergency Mapping Based on Remote Sensing Data |
| title_full_unstemmed | Techniques for the Automatic Detection and Hiding of Sensitive Targets in Emergency Mapping Based on Remote Sensing Data |
| title_short | Techniques for the Automatic Detection and Hiding of Sensitive Targets in Emergency Mapping Based on Remote Sensing Data |
| title_sort | techniques for the automatic detection and hiding of sensitive targets in emergency mapping based on remote sensing data |
| topic | emergency mapping based on remote sensing data sensitive object detection sensitive object hiding mask R-CNN model PointRend Deepfill model |
| url | https://www.mdpi.com/2220-9964/10/2/68 |
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