URBAN GREEN SPACE IDENTIFICATION BY FUSING SATELLITE IMAGES FROM GF-2 AND SENTINEL-2
This paper combines class hierarchy construction and feature-preferred random forest classification method, and the classification results are the best. In the urban center area with complex features, this method can be used to extract small and complex features more accurately, and for urban green...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2023-12-01
|
| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-archives.copernicus.org/articles/XLVIII-1-W2-2023/1405/2023/isprs-archives-XLVIII-1-W2-2023-1405-2023.pdf |
| _version_ | 1797391484521021440 |
|---|---|
| author | B. Kang G. Cai |
| author_facet | B. Kang G. Cai |
| author_sort | B. Kang |
| collection | DOAJ |
| description | This paper combines class hierarchy construction and feature-preferred random forest classification method, and the classification results are the best. In the urban center area with complex features, this method can be used to extract small and complex features more accurately, and for urban green spaces, small auxiliary green spaces between houses can be accurately extracted. This method first constructs a class hierarchy of four sizes, and then extracts different features from simple to complex, from large to small, and classifies them by membership function for the direct selection feature rules of easily extracted features. For the subdivision of green space and the classification of features in central complex areas, feature optimization is carried out, and the optimal feature combination is selected and then extreme random tree (ERT) classification is performed. The classification accuracy is the best 89.5%, and the classification results are analyzed correctly, which correctly distinguishes the smaller land categories in the central area, reduces the misclassification of grassland and agricultural land, and the classification results are optimal. |
| first_indexed | 2024-03-08T23:33:21Z |
| format | Article |
| id | doaj.art-f8b592782ba54f97969a765ee029350c |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-03-08T23:33:21Z |
| publishDate | 2023-12-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-f8b592782ba54f97969a765ee029350c2023-12-14T09:45:05ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342023-12-01XLVIII-1-W2-20231405141010.5194/isprs-archives-XLVIII-1-W2-2023-1405-2023URBAN GREEN SPACE IDENTIFICATION BY FUSING SATELLITE IMAGES FROM GF-2 AND SENTINEL-2B. Kang0G. Cai1School of Urban and Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing, ChinaSchool of Urban and Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing, ChinaThis paper combines class hierarchy construction and feature-preferred random forest classification method, and the classification results are the best. In the urban center area with complex features, this method can be used to extract small and complex features more accurately, and for urban green spaces, small auxiliary green spaces between houses can be accurately extracted. This method first constructs a class hierarchy of four sizes, and then extracts different features from simple to complex, from large to small, and classifies them by membership function for the direct selection feature rules of easily extracted features. For the subdivision of green space and the classification of features in central complex areas, feature optimization is carried out, and the optimal feature combination is selected and then extreme random tree (ERT) classification is performed. The classification accuracy is the best 89.5%, and the classification results are analyzed correctly, which correctly distinguishes the smaller land categories in the central area, reduces the misclassification of grassland and agricultural land, and the classification results are optimal.https://isprs-archives.copernicus.org/articles/XLVIII-1-W2-2023/1405/2023/isprs-archives-XLVIII-1-W2-2023-1405-2023.pdf |
| spellingShingle | B. Kang G. Cai URBAN GREEN SPACE IDENTIFICATION BY FUSING SATELLITE IMAGES FROM GF-2 AND SENTINEL-2 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | URBAN GREEN SPACE IDENTIFICATION BY FUSING SATELLITE IMAGES FROM GF-2 AND SENTINEL-2 |
| title_full | URBAN GREEN SPACE IDENTIFICATION BY FUSING SATELLITE IMAGES FROM GF-2 AND SENTINEL-2 |
| title_fullStr | URBAN GREEN SPACE IDENTIFICATION BY FUSING SATELLITE IMAGES FROM GF-2 AND SENTINEL-2 |
| title_full_unstemmed | URBAN GREEN SPACE IDENTIFICATION BY FUSING SATELLITE IMAGES FROM GF-2 AND SENTINEL-2 |
| title_short | URBAN GREEN SPACE IDENTIFICATION BY FUSING SATELLITE IMAGES FROM GF-2 AND SENTINEL-2 |
| title_sort | urban green space identification by fusing satellite images from gf 2 and sentinel 2 |
| url | https://isprs-archives.copernicus.org/articles/XLVIII-1-W2-2023/1405/2023/isprs-archives-XLVIII-1-W2-2023-1405-2023.pdf |
| work_keys_str_mv | AT bkang urbangreenspaceidentificationbyfusingsatelliteimagesfromgf2andsentinel2 AT gcai urbangreenspaceidentificationbyfusingsatelliteimagesfromgf2andsentinel2 |