An Unmixing-Based Bayesian Model for Spatio-Temporal Satellite Image Fusion in Heterogeneous Landscapes

Xue, Jie; Leung, Yee; Fung, Tung

doi:10.3390/rs11030324

Cited by 22 publications

(16 citation statements)

References 58 publications

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“…We selected four different statistical criteria to evaluate the performance of the FCMSTRFM, the ESTARFM and the STDFA: correlation coefficient (R, Equation (16)), root mean square error (RMSE, Equation (17)), the Erreur Relative Globale Adimensionalle de Synthèse (ERGAS, Equation (18)), and the mean absolute difference (MAD, Equation (19)). R indicates the linear correlativity between the observed and predicted reflectance.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…The requirement for reflectance data with both high spatial and temporal resolution is increasingly important to simulate the surface energy budget [12][13][14] and monitor ecosystem and hydrologic dynamics [15][16][17][18] at regional and global scales. In satellite design, however, a trade-off must be made between temporal and spatial resolutions [19,20].…”

Section: Introductionmentioning

confidence: 99%

“…For UMX-based methods, the abundances and endmembers are obtained by grouping high-resolution pixels, and the average reflectance of endmembers is unmixed from coarse-resolution images based on the assumption that the reflectance of each coarse spatial-resolution pixel is a linear combination of the responses of each endmember contributing to the mixture [19,33,35,36]. Generally, the accurate prediction of surface reflectance is mainly limited by grouping similar pixels and calculating the average reflectance [37,38].…”

Section: Introductionmentioning

confidence: 99%

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A Robust Method for Generating High-Spatiotemporal-Resolution Surface Reflectance by Fusing MODIS and Landsat Data

et al. 2020

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The methods for accurately fusing medium- and high-spatial-resolution satellite reflectance are vital for monitoring vegetation biomass, agricultural irrigation, ecological processes and climate change. However, the currently existing fusion methods cannot accurately capture the temporal variation in reflectance for heterogeneous landscapes. In this study, we proposed a new method, the spatial and temporal reflectance fusion method based on the unmixing theory and a fuzzy C-clustering model (FCMSTRFM), to generate Landsat-like time-series surface reflectance. Unlike other data fusion models, the FCMSTRFM improved the similarity of pixels grouped together by combining land cover maps and time-series data cluster algorithms to define endmembers. The proposed method was tested over a 2000 km2 study area in Heilongjiang Provence, China, in 2017 and 2018 using ten images. The results show that the accuracy of the FCMSTRFM is better than that of the popular enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) (correlation coefficient (R): 0.8413 vs. 0.7589; root mean square error (RMSE): 0.0267 vs. 0.0401) and the spatial-temporal data fusion approach (STDFA) (R: 0.8413 vs. 0.7666; RMSE: 0.0267 vs. 0.0307). Importantly, the FCMSTRFM was able to maintain the details of temporal variations in complicated landscapes. The proposed method provides an alternative method to monitor the dynamics of land surface variables over complicated heterogeneous regions.

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Section: Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Robust Method for Generating High-Spatiotemporal-Resolution Surface Reflectance by Fusing MODIS and Landsat Data

et al. 2020

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“…In addition to the above algorithms, there are other fusion approaches that incorporate the unmixing based method to improve the performance of existing algorithms. For example, two improved Bayesian data fusion approaches (ISTBDF-I and -II) [31] were proposed by incorporating an unmixing based algorithm into the existing Spatiotemporal Bayesian Data Fusion (STBDF) framework [32], which can enhance the fusion ability of existing STBDF model in handling heterogeneous areas.…”

Section: Introductionmentioning

confidence: 99%

Adaptive-SFSDAF for Spatiotemporal Image Fusion that Selectively Uses Class Abundance Change Information

et al. 2020

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Many spatiotemporal image fusion methods in remote sensing have been developed to blend highly resolved spatial images and highly resolved temporal images to solve the problem of a trade-off between the spatial and temporal resolution from a single sensor. Yet, none of the spatiotemporal fusion methods considers how the various temporal changes between different pixels affect the performance of the fusion results; to develop an improved fusion method, these temporal changes need to be integrated into one framework. Adaptive-SFSDAF extends the existing fusion method that incorporates sub-pixel class fraction change information in Flexible Spatiotemporal DAta Fusion (SFSDAF) by modifying spectral unmixing to select spectral unmixing adaptively in order to greatly improve the efficiency of the algorithm. Accordingly, the main contributions of the proposed adaptive-SFSDAF method are twofold. One is to address the detection of outliers of temporal change in the image during the period between the origin and prediction dates, as these pixels are the most difficult to estimate and affect the performance of the spatiotemporal fusion methods. The other primary contribution is to establish an adaptive unmixing strategy according to the guided mask map, thus effectively eliminating a great number of insignificant unmixed pixels. The proposed method is compared with the state-of-the-art Flexible Spatiotemporal DAta Fusion (FSDAF), SFSDAF, FIT-FC, and Unmixing-Based Data Fusion (UBDF) methods, and the fusion accuracy is evaluated both quantitatively and visually. The experimental results show that adaptive-SFSDAF achieves outstanding performance in balancing computational efficiency and the accuracy of the fusion results.

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“…Its spatial resolution, however, is not fine enough to capture the spatial details required over heterogeneous regions. In the past decade, a number of spatiotemporal data fusion algorithms have been developed to combine satellite images such as these to generate synthetic data with both high spatial and high temporal resolution [11][12][13][14][15][16][17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of High-Temporal- and High-Spatial-Resolution Reflectance Datasets Using Difference Construction and Bayesian Unmixing

Yang

Song

Han³

et al. 2020

Remote Sensing

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High-temporal- and high-spatial-resolution reflectance datasets play a vital role in monitoring dynamic changes at the Earth’s land surface. So far, many sensors have been designed with a trade-off between swath width and pixel size; thus, it is difficult to obtain reflectance data with both high spatial resolution and frequent coverage from a single sensor. In this study, we propose a new Reflectance Bayesian Spatiotemporal Fusion Model (Ref-BSFM) using Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) surface reflectance, which is then used to construct reflectance datasets with high spatiotemporal resolution and a long time series. By comparing this model with other popular reconstruction methods (the Flexible Spatiotemporal Data Fusion Model, the Spatial and Temporal Adaptive Reflectance Fusion Model, and the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model), we demonstrate that our approach has the following advantages: (1) higher prediction accuracy, (2) effective treatment of cloud coverage, (3) insensitivity to the time span of data acquisition, (4) capture of temporal change information, and (5) higher retention of spatial details and inconspicuous MODIS patches. Reflectance time-series datasets generated by Ref-BSFM can be used to calculate a variety of remote-sensing-based vegetation indices, providing an important data source for land surface dynamic monitoring.

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An Unmixing-Based Bayesian Model for Spatio-Temporal Satellite Image Fusion in Heterogeneous Landscapes

Cited by 22 publications

References 58 publications

A Robust Method for Generating High-Spatiotemporal-Resolution Surface Reflectance by Fusing MODIS and Landsat Data

A Robust Method for Generating High-Spatiotemporal-Resolution Surface Reflectance by Fusing MODIS and Landsat Data

Adaptive-SFSDAF for Spatiotemporal Image Fusion that Selectively Uses Class Abundance Change Information

Reconstruction of High-Temporal- and High-Spatial-Resolution Reflectance Datasets Using Difference Construction and Bayesian Unmixing

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