Abstract
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.
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Subject
Earth and Planetary Sciences (miscellaneous),Computers in Earth Sciences,Geography, Planning and Development
Reference50 articles.
1. Key technologies of emergency surveying and mapping service system;Zhu;Geomat. Inf. Sci. Wuhan Univ.,2014
2. Based on remote sensing drawing monitoring, evaluate and father flood;Yang;Map,1998
3. Study on urgent monitoring and assessment in Wenchuan earthquake;Fan;J. Remote Sens.,2008
4. Implementation of remote sensing automatic mapping used for earthquake emergency;Xu;J. Nat. Disasters,2017
5. Automatic detection of earthquake-induced ground failure effects through Faster R-CNN deep learning-based object detection using satellite images
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献