A Method for Classifying Complex Features in Urban Areas Using Video Satellite Remote Sensing Data
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 challen...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/10/2324 |
| _version_ | 1797495939148021760 |
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| author | Fanghong Ye Tinghua Ai Jiaming Wang Yuan Yao Zheng Zhou |
| author_facet | Fanghong Ye Tinghua Ai Jiaming Wang Yuan Yao Zheng Zhou |
| author_sort | Fanghong Ye |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T01:57:39Z |
| format | Article |
| id | doaj.art-32ace8f432674073889ef856043807f6 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T01:57:39Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-32ace8f432674073889ef856043807f62023-11-23T12:54:14ZengMDPI AGRemote Sensing2072-42922022-05-011410232410.3390/rs14102324A Method for Classifying Complex Features in Urban Areas Using Video Satellite Remote Sensing DataFanghong Ye0Tinghua Ai1Jiaming Wang2Yuan Yao3Zheng Zhou4School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaThe State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, ChinaThe State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, ChinaEcology and Environment Monitoring and Scientific Research Center, Yangtze Basin Ecology and Environment Administration, Ministry of Ecology and Environment of the P.R. China, Wuhan 430014, ChinaThe 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.https://www.mdpi.com/2072-4292/14/10/2324video satelliteremote sensingclassificationurban areacomplex features |
| spellingShingle | Fanghong Ye Tinghua Ai Jiaming Wang Yuan Yao Zheng Zhou A Method for Classifying Complex Features in Urban Areas Using Video Satellite Remote Sensing Data Remote Sensing video satellite remote sensing classification urban area complex features |
| title | A Method for Classifying Complex Features in Urban Areas Using Video Satellite Remote Sensing Data |
| title_full | A Method for Classifying Complex Features in Urban Areas Using Video Satellite Remote Sensing Data |
| title_fullStr | A Method for Classifying Complex Features in Urban Areas Using Video Satellite Remote Sensing Data |
| title_full_unstemmed | A Method for Classifying Complex Features in Urban Areas Using Video Satellite Remote Sensing Data |
| title_short | A Method for Classifying Complex Features in Urban Areas Using Video Satellite Remote Sensing Data |
| title_sort | method for classifying complex features in urban areas using video satellite remote sensing data |
| topic | video satellite remote sensing classification urban area complex features |
| url | https://www.mdpi.com/2072-4292/14/10/2324 |
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