A Bayesian Data Fusion Approach to Spatio-Temporal Fusion of Remotely Sensed Images
Remote sensing provides rich sources of data for the monitoring of land surface dynamics. However, single-sensor systems are constrained from providing spatially high-resolution images with high revisit frequency due to the inherent sensor design limitation. To obtain images high in both spatial and...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/9/12/1310 |
| _version_ | 1819107262486020096 |
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| author | Jie Xue Yee Leung Tung Fung |
| author_facet | Jie Xue Yee Leung Tung Fung |
| author_sort | Jie Xue |
| collection | DOAJ |
| description | Remote sensing provides rich sources of data for the monitoring of land surface dynamics. However, single-sensor systems are constrained from providing spatially high-resolution images with high revisit frequency due to the inherent sensor design limitation. To obtain images high in both spatial and temporal resolutions, a number of image fusion algorithms, such as spatial and temporal adaptive reflectance fusion model (STARFM) and enhanced STARFM (ESTARFM), have been recently developed. To capitalize on information available in a fusion process, we propose a Bayesian data fusion approach that incorporates the temporal correlation information in the image time series and casts the fusion problem as an estimation problem in which the fused image is obtained by the Maximum A Posterior (MAP) estimator. The proposed approach provides a formal framework for the fusion of remotely sensed images with a rigorous statistical basis; it imposes no requirements on the number of input image pairs; and it is suitable for heterogeneous landscapes. The approach is empirically tested with both simulated and real-life acquired Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) images. Experimental results demonstrate that the proposed method outperforms STARFM and ESTARFM, especially for heterogeneous landscapes. It produces surface reflectances highly correlated with those of the reference Landsat images. It gives spatio-temporal fusion of remotely sensed images a solid theoretical and empirical foundation that may be extended to solve more complicated image fusion problems. |
| first_indexed | 2024-12-22T02:51:15Z |
| format | Article |
| id | doaj.art-96b7dbfcc10246d998c1c4eae2d21627 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-22T02:51:15Z |
| publishDate | 2017-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-96b7dbfcc10246d998c1c4eae2d216272022-12-21T18:41:23ZengMDPI AGRemote Sensing2072-42922017-12-01912131010.3390/rs9121310rs9121310A Bayesian Data Fusion Approach to Spatio-Temporal Fusion of Remotely Sensed ImagesJie Xue0Yee Leung1Tung Fung2Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, ChinaDepartment of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, ChinaDepartment of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, ChinaRemote sensing provides rich sources of data for the monitoring of land surface dynamics. However, single-sensor systems are constrained from providing spatially high-resolution images with high revisit frequency due to the inherent sensor design limitation. To obtain images high in both spatial and temporal resolutions, a number of image fusion algorithms, such as spatial and temporal adaptive reflectance fusion model (STARFM) and enhanced STARFM (ESTARFM), have been recently developed. To capitalize on information available in a fusion process, we propose a Bayesian data fusion approach that incorporates the temporal correlation information in the image time series and casts the fusion problem as an estimation problem in which the fused image is obtained by the Maximum A Posterior (MAP) estimator. The proposed approach provides a formal framework for the fusion of remotely sensed images with a rigorous statistical basis; it imposes no requirements on the number of input image pairs; and it is suitable for heterogeneous landscapes. The approach is empirically tested with both simulated and real-life acquired Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) images. Experimental results demonstrate that the proposed method outperforms STARFM and ESTARFM, especially for heterogeneous landscapes. It produces surface reflectances highly correlated with those of the reference Landsat images. It gives spatio-temporal fusion of remotely sensed images a solid theoretical and empirical foundation that may be extended to solve more complicated image fusion problems.https://www.mdpi.com/2072-4292/9/12/1310Bayesian data fusionLandsatMODISspatio-temporal image fusiontime series |
| spellingShingle | Jie Xue Yee Leung Tung Fung A Bayesian Data Fusion Approach to Spatio-Temporal Fusion of Remotely Sensed Images Remote Sensing Bayesian data fusion Landsat MODIS spatio-temporal image fusion time series |
| title | A Bayesian Data Fusion Approach to Spatio-Temporal Fusion of Remotely Sensed Images |
| title_full | A Bayesian Data Fusion Approach to Spatio-Temporal Fusion of Remotely Sensed Images |
| title_fullStr | A Bayesian Data Fusion Approach to Spatio-Temporal Fusion of Remotely Sensed Images |
| title_full_unstemmed | A Bayesian Data Fusion Approach to Spatio-Temporal Fusion of Remotely Sensed Images |
| title_short | A Bayesian Data Fusion Approach to Spatio-Temporal Fusion of Remotely Sensed Images |
| title_sort | bayesian data fusion approach to spatio temporal fusion of remotely sensed images |
| topic | Bayesian data fusion Landsat MODIS spatio-temporal image fusion time series |
| url | https://www.mdpi.com/2072-4292/9/12/1310 |
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