标题: A Method for Classifying Complex Features in Urban Areas Using Video Satellite Remote Sensing Data

作者: Ye, FH (Ye, Fanghong); Ai, TH (Ai, Tinghua); Wang, JM (Wang, Jiaming); Yao, Y (Yao, Yuan); Zhou, Z (Zhou, Zheng)

来源出版物: REMOTE SENSING 卷: 14 期: 10 文献号: 2324 DOI: 10.3390/rs14102324 出版年: MAY 2022

摘要: The classification of optical satellite-derived remote sensing images is an important satellite remote sensing application. Due to the wide variety of artificial features and complex ground situations in urban areas, the classification of complex urban features has always been a focus of and challenge in the field of remote sensing image classification. Given the limited information that can be obtained from traditional optical satellite-derived remote sensing data of a classification area, it is difficult to classify artificial features in detail at the pixel level. With the development of technologies, such as satellite platforms and sensors, the data types acquired by remote sensing satellites have evolved from static images to dynamic videos. Compared with traditional satellite-derived images, satellite-derived videos contain increased ground object reflection information, especially information obtained from different observation angles, and can thus provide more information for classifying complex urban features and improving the corresponding classification accuracies. In this paper, first, we analyze urban-area, ground feature characteristics and satellite-derived video remote sensing data. Second, according to these characteristics, we design a pixel-level classification method based on the application of machine learning techniques to video remote sensing data that represents complex, urban-area ground features. Last, we conduct experiments on real data. The test results show that applying the method designed in this paper to classify dynamic, satellite-derived video remote sensing data can improve the classification accuracy of complex features in urban areas compared with the classification results obtained using static, satellite-derived remote sensing image data at the same resolution.

作者关键词: video satellite; remote sensing; classification; urban area; complex features

地址: [Ye, Fanghong; Ai, Tinghua] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Ye, Fanghong] Minist Nat Resources PR China, Land Satellite Remote Sensing Applicat Ctr, Beijing 100048, Peoples R China.

[Wang, Jiaming; Yao, Yuan] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China.

[Yao, Yuan] Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Peoples R China.

[Zhou, Zheng] Minist Ecol & Environm PR China, Ecol & Environm Monitoring & Sci Res Ctr, Yangtze Basin Ecol & Environm Adm, Wuhan 430014, Peoples R China.

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

电子邮件地址: yefh@lasac.cn; tinghuaai@whu.edu.cn; wjmecho@whu.edu.cn; whyaoyuan@whu.edu.cn; zhouzheng@cjjg.mee.gov.cn

影响因子：4.848