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Improving Urban Land Cover Classification with Combined Use of Sentinel-2 and Sentinel-1 Imagery
| Content Provider | MDPI |
|---|---|
| Author | Hu, Bin Xu, Yongyang Huang, Xiao Cheng, Qimin Ding, Qing Bai, Linze Li, Yan |
| Copyright Year | 2021 |
| Description | Accurate land cover mapping is important for urban planning and management. Remote sensing data have been widely applied for urban land cover mapping. However, obtaining land cover classification via optical remote sensing data alone is difficult due to spectral confusion. To reduce the confusion between dark impervious surface and water, the Sentinel-1A Synthetic Aperture Rader (SAR) data are synergistically combined with the Sentinel-2B Multispectral Instrument (MSI) data. The novel support vector machine with composite kernels (SVM-CK) approach, which can exploit the spatial information, is proposed to process the combination of Sentinel-2B MSI and Sentinel-1A SAR data. The classification based on the fusion of Sentinel-2B and Sentinel-1A data yields an overall accuracy (OA) of 92.12% with a kappa coefficient (KA) of 0.89, superior to the classification results using Sentinel-2B MSI imagery and Sentinel-1A SAR imagery separately. The results indicate that the inclusion of Sentinel-1A SAR data to Sentinel-2B MSI data can improve the classification performance by reducing the confusion between built-up area and water. This study shows that the land cover classification can be improved by fusing Sentinel-2B and Sentinel-1A imagery. |
| Starting Page | 533 |
| e-ISSN | 22209964 |
| DOI | 10.3390/ijgi10080533 |
| Journal | ISPRS International Journal of Geo-Information |
| Issue Number | 8 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-08-09 |
| Access Restriction | Open |
| Subject Keyword | ISPRS International Journal of Geo-Information Isprs International Journal of Geo-information Remote Sensing Sentinel-2b Sentinel-1a Land Cover Classification Support Vector Machine Data Fusion |
| Content Type | Text |
| Resource Type | Article |
