A superresolution land-cover change detection method using remotely sensed images with different spatial resolutions
The development of remote sensing has enabled the acquisition of information on land-cover change at different spatial scales. However, a trade-off between spatial and temporal resolutions normally exists. Fine-spatial-resolution images have low temporal resolutions, whereas coarse spatial resolutio...
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| Format: | Article |
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Institute of Electrical and Electronics Engineers
2016
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| Online Access: | https://eprints.nottingham.ac.uk/32947/ |
| _version_ | 1848794526062215168 |
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| author | Li, Xiaodong Ling, Feng Foody, Giles M. Du, Yun |
| author_facet | Li, Xiaodong Ling, Feng Foody, Giles M. Du, Yun |
| author_sort | Li, Xiaodong |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The development of remote sensing has enabled the acquisition of information on land-cover change at different spatial scales. However, a trade-off between spatial and temporal resolutions normally exists. Fine-spatial-resolution images have low temporal resolutions, whereas coarse spatial resolution images have high temporal repetition rates. A novel super-resolution change detection method (SRCD)is proposed to detect land-cover changes at both fine spatial and temporal resolutions with the use of a coarse-resolution image and a fine-resolution land-cover map acquired at different times. SRCD is an iterative method that involves endmember estimation, spectral unmixing, land-cover fraction change detection, and super-resolution land-cover mapping. Both the land-cover change/no-change map and from–to change map at fine spatial resolution can be generated by SRCD. In this study, SRCD was applied to synthetic multispectral image, Moderate-Resolution Imaging Spectroradiometer (MODIS) multispectral image and Landsat-8 Operational Land Imager (OLI) multispectral image. The land-cover from–to change maps are found to have the highest overall accuracy (higher than 85%) in all the three experiments. Most of the changed land-cover patches, which were larger than the coarse-resolution pixel, were correctly detected. |
| first_indexed | 2025-11-14T19:17:35Z |
| format | Article |
| id | nottingham-32947 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:17:35Z |
| publishDate | 2016 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-329472020-05-04T17:50:48Z https://eprints.nottingham.ac.uk/32947/ A superresolution land-cover change detection method using remotely sensed images with different spatial resolutions Li, Xiaodong Ling, Feng Foody, Giles M. Du, Yun The development of remote sensing has enabled the acquisition of information on land-cover change at different spatial scales. However, a trade-off between spatial and temporal resolutions normally exists. Fine-spatial-resolution images have low temporal resolutions, whereas coarse spatial resolution images have high temporal repetition rates. A novel super-resolution change detection method (SRCD)is proposed to detect land-cover changes at both fine spatial and temporal resolutions with the use of a coarse-resolution image and a fine-resolution land-cover map acquired at different times. SRCD is an iterative method that involves endmember estimation, spectral unmixing, land-cover fraction change detection, and super-resolution land-cover mapping. Both the land-cover change/no-change map and from–to change map at fine spatial resolution can be generated by SRCD. In this study, SRCD was applied to synthetic multispectral image, Moderate-Resolution Imaging Spectroradiometer (MODIS) multispectral image and Landsat-8 Operational Land Imager (OLI) multispectral image. The land-cover from–to change maps are found to have the highest overall accuracy (higher than 85%) in all the three experiments. Most of the changed land-cover patches, which were larger than the coarse-resolution pixel, were correctly detected. Institute of Electrical and Electronics Engineers 2016-05-24 Article PeerReviewed Li, Xiaodong, Ling, Feng, Foody, Giles M. and Du, Yun (2016) A superresolution land-cover change detection method using remotely sensed images with different spatial resolutions. IEEE Transactions on Geoscience and Remote Sensing, 54 (7). pp. 3822-3841. ISSN 0196-2892 Land-cover change detection; super-resolution mapping; the mixed pixel problem. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7426366 doi:10.1109/TGRS.2016.2528583 doi:10.1109/TGRS.2016.2528583 |
| spellingShingle | Land-cover change detection; super-resolution mapping; the mixed pixel problem. Li, Xiaodong Ling, Feng Foody, Giles M. Du, Yun A superresolution land-cover change detection method using remotely sensed images with different spatial resolutions |
| title | A superresolution land-cover change detection method using remotely sensed images with different spatial resolutions |
| title_full | A superresolution land-cover change detection method using remotely sensed images with different spatial resolutions |
| title_fullStr | A superresolution land-cover change detection method using remotely sensed images with different spatial resolutions |
| title_full_unstemmed | A superresolution land-cover change detection method using remotely sensed images with different spatial resolutions |
| title_short | A superresolution land-cover change detection method using remotely sensed images with different spatial resolutions |
| title_sort | superresolution land-cover change detection method using remotely sensed images with different spatial resolutions |
| topic | Land-cover change detection; super-resolution mapping; the mixed pixel problem. |
| url | https://eprints.nottingham.ac.uk/32947/ https://eprints.nottingham.ac.uk/32947/ https://eprints.nottingham.ac.uk/32947/ |