Spatial enhanced spatiotemporal reflectance fusion model for heterogeneous regions with land cover change
Numerous spatiotemporal fusion models have been developed to fuse dense time-series data with a high spatial resolution for monitoring land surface dynamics. Nonetheless, enhancing spatial details of fused images, eliminating the obvious ‘plaque’ phenomenon and image blurring in fused images, and de...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-08-01
|
| Series: | Geocarto International |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/10106049.2023.2250331 |
| _version_ | 1797681593653919744 |
|---|---|
| author | Xinyu Pi Wei Huang Yongnian Zeng Pancheng Wang |
| author_facet | Xinyu Pi Wei Huang Yongnian Zeng Pancheng Wang |
| author_sort | Xinyu Pi |
| collection | DOAJ |
| description | Numerous spatiotemporal fusion models have been developed to fuse dense time-series data with a high spatial resolution for monitoring land surface dynamics. Nonetheless, enhancing spatial details of fused images, eliminating the obvious ‘plaque’ phenomenon and image blurring in fused images, and developing relatively simple and easy-to-implement algorithms remain a challenge for spatiotemporal fusion algorithms. Therefore, this paper presents a newly proposed spatial enhanced spatiotemporal reflectance fusion model (SE-STRFM) for image fusions in heterogeneous regions with land cover change. The SE-STRFM model predicts temporal changes of reflectance in sub-pixel details based on the spectral unmixing theory, and allocates reflectance changes caused by abrupt land cover change in fine-resolution images with a relatively simple algorithm and easy implementation. SE-STRFM only needs one pair of input data, comprising one fine-resolution image and one coarse-resolution image, to achieve high-precision reflectance prediction with spatial details. To verify the reliability and applicability of the SE-STRFM, we use Landsat image and simulated MODIS-like image to fuse high spatial and temporal resolution images and select two study areas with heterogeneous landscape and land cover type change for fusion experiments and accuracy evaluation. The results show that the images fused by SE-STRFM have clearer spatial details and a more accurate spectral distribution compared with those fused by the most widely used STARFM, ESTARFM and FSDAF. In two study areas with heterogeneous landscape and land cover type change, compared with STARFM, ESTARFM and FSDAF, the RMSE of SE-STRFM is 10.52%, 28.39% and 6.58% lower on average, respectively; r is 3.67%, 10.33% and 1.65% higher on average, respectively; AAD is 9.05%, 24.58% and 7.29% lower on average, respectively; and SSIM is 3.16%, 10.16% and 1.92% higher on average, respectively. SE-STRFM can accurately capture temporal changes with spatial details and effectively predict abrupt land-cover changes. |
| first_indexed | 2024-03-11T23:47:12Z |
| format | Article |
| id | doaj.art-9216135f8f5843578575fd024abe6700 |
| institution | Directory Open Access Journal |
| issn | 1010-6049 1752-0762 |
| language | English |
| last_indexed | 2024-03-11T23:47:12Z |
| publishDate | 2023-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj.art-9216135f8f5843578575fd024abe67002023-09-19T09:13:18ZengTaylor & Francis GroupGeocarto International1010-60491752-07622023-08-0138110.1080/10106049.2023.22503312250331Spatial enhanced spatiotemporal reflectance fusion model for heterogeneous regions with land cover changeXinyu Pi0Wei Huang1Yongnian Zeng2Pancheng Wang3Center for Geomatics and Regional Sustainable Development Research, Central South UniversityCollege of Surveying & Geo-Informatics, Tongji UniversityCenter for Geomatics and Regional Sustainable Development Research, Central South UniversityCenter for Geomatics and Regional Sustainable Development Research, Central South UniversityNumerous spatiotemporal fusion models have been developed to fuse dense time-series data with a high spatial resolution for monitoring land surface dynamics. Nonetheless, enhancing spatial details of fused images, eliminating the obvious ‘plaque’ phenomenon and image blurring in fused images, and developing relatively simple and easy-to-implement algorithms remain a challenge for spatiotemporal fusion algorithms. Therefore, this paper presents a newly proposed spatial enhanced spatiotemporal reflectance fusion model (SE-STRFM) for image fusions in heterogeneous regions with land cover change. The SE-STRFM model predicts temporal changes of reflectance in sub-pixel details based on the spectral unmixing theory, and allocates reflectance changes caused by abrupt land cover change in fine-resolution images with a relatively simple algorithm and easy implementation. SE-STRFM only needs one pair of input data, comprising one fine-resolution image and one coarse-resolution image, to achieve high-precision reflectance prediction with spatial details. To verify the reliability and applicability of the SE-STRFM, we use Landsat image and simulated MODIS-like image to fuse high spatial and temporal resolution images and select two study areas with heterogeneous landscape and land cover type change for fusion experiments and accuracy evaluation. The results show that the images fused by SE-STRFM have clearer spatial details and a more accurate spectral distribution compared with those fused by the most widely used STARFM, ESTARFM and FSDAF. In two study areas with heterogeneous landscape and land cover type change, compared with STARFM, ESTARFM and FSDAF, the RMSE of SE-STRFM is 10.52%, 28.39% and 6.58% lower on average, respectively; r is 3.67%, 10.33% and 1.65% higher on average, respectively; AAD is 9.05%, 24.58% and 7.29% lower on average, respectively; and SSIM is 3.16%, 10.16% and 1.92% higher on average, respectively. SE-STRFM can accurately capture temporal changes with spatial details and effectively predict abrupt land-cover changes.http://dx.doi.org/10.1080/10106049.2023.2250331remote sensingspatiotemporal fusionspatial enhanced modelheterogeneous landscapeland cover change |
| spellingShingle | Xinyu Pi Wei Huang Yongnian Zeng Pancheng Wang Spatial enhanced spatiotemporal reflectance fusion model for heterogeneous regions with land cover change Geocarto International remote sensing spatiotemporal fusion spatial enhanced model heterogeneous landscape land cover change |
| title | Spatial enhanced spatiotemporal reflectance fusion model for heterogeneous regions with land cover change |
| title_full | Spatial enhanced spatiotemporal reflectance fusion model for heterogeneous regions with land cover change |
| title_fullStr | Spatial enhanced spatiotemporal reflectance fusion model for heterogeneous regions with land cover change |
| title_full_unstemmed | Spatial enhanced spatiotemporal reflectance fusion model for heterogeneous regions with land cover change |
| title_short | Spatial enhanced spatiotemporal reflectance fusion model for heterogeneous regions with land cover change |
| title_sort | spatial enhanced spatiotemporal reflectance fusion model for heterogeneous regions with land cover change |
| topic | remote sensing spatiotemporal fusion spatial enhanced model heterogeneous landscape land cover change |
| url | http://dx.doi.org/10.1080/10106049.2023.2250331 |
| work_keys_str_mv | AT xinyupi spatialenhancedspatiotemporalreflectancefusionmodelforheterogeneousregionswithlandcoverchange AT weihuang spatialenhancedspatiotemporalreflectancefusionmodelforheterogeneousregionswithlandcoverchange AT yongnianzeng spatialenhancedspatiotemporalreflectancefusionmodelforheterogeneousregionswithlandcoverchange AT panchengwang spatialenhancedspatiotemporalreflectancefusionmodelforheterogeneousregionswithlandcoverchange |