标题: Techniques for the Automatic Detection and Hiding of Sensitive Targets in Emergency Mapping Based on Remote Sensing Data

作者: Qiu, TQ (Qiu, Tianqi); Liang, XJ (Liang, Xiaojin); Du, QY (Du, Qingyun); Ren, F (Ren, Fu); Lu, PJ (Lu, Pengjie); Wu, C (Wu, Chao)

来源出版物: ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION卷: 10期: 2文献号: 68 DOI: 10.3390/ijgi10020068出版年: FEB 2021

摘要: 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.

入藏号: WOS:000622572500001

语言: English

文献类型: Article

作者关键词: emergency mapping based on remote sensing data; sensitive object detection; sensitive object hiding; mask R-CNN model; PointRend; Deepfill model

地址: [Qiu, Tianqi; Liang, Xiaojin; Du, Qingyun; Ren, Fu; Lu, Pengjie] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Du, Qingyun; Ren, Fu] Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan 430079, Peoples R China.

[Du, Qingyun] Wuhan Univ, Key Lab Digital Mapping & Land Informat Applicat, Natl Adm Surveying Mapping & Geoinformat, Wuhan 430079, Peoples R China.

[Du, Qingyun] Wuhan Univ, Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Peoples R China.

[Wu, Chao] Nanjing Univ Posts & Telecommun, Sch Geog & Biol Informat, Nanjing 210023, Peoples R China.

通讯作者地址: Du, QY (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

Du, QY (通讯作者)，Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan 430079, Peoples R China.

Du, QY (通讯作者)，Wuhan Univ, Key Lab Digital Mapping & Land Informat Applicat, Natl Adm Surveying Mapping & Geoinformat, Wuhan 430079, Peoples R China.

Du, QY (通讯作者)，Wuhan Univ, Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Peoples R China.

电子邮件地址: qtq@whu.edu.cn; liangxj@whu.edu.cn; qydu@whu.edu.cn; renfu@whu.edu.cn; lupengjie@whu.edu.cn;chaowu@njupt.edu.cn

影响因子：2.239