Exploring the Impact of Seasonality on Urban Land-Cover Mapping Using Multi-Season Sentinel-1A and GF-1 WFV Images in a Subtropical Monsoon-Climate Region
The objective of this research was to investigate the impact of seasonality on urban land-cover mapping and to explore better classification accuracy by using multi-season Sentinel-1A and GF-1 wide field view (WFV) images, and the combinations of both types of images in subtropical monsoon-climate r...
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MDPI AG
2017-12-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/7/1/3 |
| _version_ | 1811203629458128896 |
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| author | Tao Zhou Meifang Zhao Chuanliang Sun Jianjun Pan |
| author_facet | Tao Zhou Meifang Zhao Chuanliang Sun Jianjun Pan |
| author_sort | Tao Zhou |
| collection | DOAJ |
| description | The objective of this research was to investigate the impact of seasonality on urban land-cover mapping and to explore better classification accuracy by using multi-season Sentinel-1A and GF-1 wide field view (WFV) images, and the combinations of both types of images in subtropical monsoon-climate regions in Southeast China. We obtained multi-season Sentinel-1A and GF-1 WFV images, as well as the combinations of both data, by using a support vector machine (SVM) and a random forest (RF) classifier. The backscatter intensity, texture, and interference-coherence images were extracted from Sentinel-1A images, and different combinations of these Sentinel-1A-derived images were used to evaluate their ability to map urban land cover. The results showed that the performance of winter images was better than that of any other season, while the summer images performed the worst. Higher classification accuracy was achieved by using multi-season images, and satisfactory classification results were obtained when using Sentinel-1A images from only three seasons. The best classification result was achieved using a combination of all Sentinel-1A data from all four seasons and GF-1 WFV data from winter, with an overall accuracy of up to 96.02% and a kappa coefficient reaching 0.9502. The performance of textures was slightly better than that of the backscatter-intensity images. Although the coherence data performed the worst, it was still able to distinguish urban impervious surfaces well. In addition, the overall classification accuracy of RF was better than that of SVM. |
| first_indexed | 2024-04-12T02:58:29Z |
| format | Article |
| id | doaj.art-626ec1f9cd374897af8636727eddaac4 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-04-12T02:58:29Z |
| publishDate | 2017-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-626ec1f9cd374897af8636727eddaac42022-12-22T03:50:43ZengMDPI AGISPRS International Journal of Geo-Information2220-99642017-12-0171310.3390/ijgi7010003ijgi7010003Exploring the Impact of Seasonality on Urban Land-Cover Mapping Using Multi-Season Sentinel-1A and GF-1 WFV Images in a Subtropical Monsoon-Climate RegionTao Zhou0Meifang Zhao1Chuanliang Sun2Jianjun Pan3College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaOffice of Baima Campus Construction, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaThe objective of this research was to investigate the impact of seasonality on urban land-cover mapping and to explore better classification accuracy by using multi-season Sentinel-1A and GF-1 wide field view (WFV) images, and the combinations of both types of images in subtropical monsoon-climate regions in Southeast China. We obtained multi-season Sentinel-1A and GF-1 WFV images, as well as the combinations of both data, by using a support vector machine (SVM) and a random forest (RF) classifier. The backscatter intensity, texture, and interference-coherence images were extracted from Sentinel-1A images, and different combinations of these Sentinel-1A-derived images were used to evaluate their ability to map urban land cover. The results showed that the performance of winter images was better than that of any other season, while the summer images performed the worst. Higher classification accuracy was achieved by using multi-season images, and satisfactory classification results were obtained when using Sentinel-1A images from only three seasons. The best classification result was achieved using a combination of all Sentinel-1A data from all four seasons and GF-1 WFV data from winter, with an overall accuracy of up to 96.02% and a kappa coefficient reaching 0.9502. The performance of textures was slightly better than that of the backscatter-intensity images. Although the coherence data performed the worst, it was still able to distinguish urban impervious surfaces well. In addition, the overall classification accuracy of RF was better than that of SVM.https://www.mdpi.com/2220-9964/7/1/3seasonalitymulti-season imagesGF-1Sentinel-1urban classification |
| spellingShingle | Tao Zhou Meifang Zhao Chuanliang Sun Jianjun Pan Exploring the Impact of Seasonality on Urban Land-Cover Mapping Using Multi-Season Sentinel-1A and GF-1 WFV Images in a Subtropical Monsoon-Climate Region ISPRS International Journal of Geo-Information seasonality multi-season images GF-1 Sentinel-1 urban classification |
| title | Exploring the Impact of Seasonality on Urban Land-Cover Mapping Using Multi-Season Sentinel-1A and GF-1 WFV Images in a Subtropical Monsoon-Climate Region |
| title_full | Exploring the Impact of Seasonality on Urban Land-Cover Mapping Using Multi-Season Sentinel-1A and GF-1 WFV Images in a Subtropical Monsoon-Climate Region |
| title_fullStr | Exploring the Impact of Seasonality on Urban Land-Cover Mapping Using Multi-Season Sentinel-1A and GF-1 WFV Images in a Subtropical Monsoon-Climate Region |
| title_full_unstemmed | Exploring the Impact of Seasonality on Urban Land-Cover Mapping Using Multi-Season Sentinel-1A and GF-1 WFV Images in a Subtropical Monsoon-Climate Region |
| title_short | Exploring the Impact of Seasonality on Urban Land-Cover Mapping Using Multi-Season Sentinel-1A and GF-1 WFV Images in a Subtropical Monsoon-Climate Region |
| title_sort | exploring the impact of seasonality on urban land cover mapping using multi season sentinel 1a and gf 1 wfv images in a subtropical monsoon climate region |
| topic | seasonality multi-season images GF-1 Sentinel-1 urban classification |
| url | https://www.mdpi.com/2220-9964/7/1/3 |
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