A model for spatial and temporal data fusion

Wu, Mingquan; Wang, Jie; Niu, Zheng; Zhao, Yue; Wang, Changyao

doi:10.3724/sp.j.1010.2012.00080

Cited by 16 publications

(13 citation statements)

References 8 publications

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“…What is worthy of mentioning is that the STDFM algorithm tested in this paper applies the same classification method as ESTDFM (i.e., patch-based ISODATA classification) to get the classification map. Although a different classification method has been applied in the quoted literature (i.e., reference [16]), the essence of the STDFM algorithm in this paper is consistent.…”

Section: Evaluation Methodssupporting

confidence: 60%

“…As with some previous studies [16,18], the point spread function (PSF) [29,30] is not considered in the calculation of the abundance of endmembers, as mentioned in Section 3.2.2. The calculated abundance is actually the area proportion of each class within a certain LSR pixel.…”

Section: Point Spread Functionmentioning

confidence: 99%

“…The algorithm has been proven to be effective in multiple studies [14,15], based on the assumption that the spectral properties of an endmember are almost constant in adjacent pixels (hypothesis (1)). However, the algorithm, or some other similar algorithms, can only get the mean reflectance of different endmembers due to its own limitations (i.e., hypothesis (1)) [16], although some scholars introduced spatial distance [17] or spectral distance [18] in a weighted scheme for higher unmixed precisions. Wu et al [16] proposed the Spatial and Temporal Data Fusion Model (STDFM) algorithm (described in detail in Sections 2.2 and 2.3), which can obtain the reflectance of different endmembers.…”

mentioning

confidence: 99%

“…However, the algorithm, or some other similar algorithms, can only get the mean reflectance of different endmembers due to its own limitations (i.e., hypothesis (1)) [16], although some scholars introduced spatial distance [17] or spectral distance [18] in a weighted scheme for higher unmixed precisions. Wu et al [16] proposed the Spatial and Temporal Data Fusion Model (STDFM) algorithm (described in detail in Sections 2.2 and 2.3), which can obtain the reflectance of different endmembers. Though the algorithm can be used to produce a synthetic reflectance product with both HSR and frequent coverage [16], it still has some shortcomings in theory.…”

mentioning

confidence: 99%

“…Wu et al [16] proposed the Spatial and Temporal Data Fusion Model (STDFM) algorithm (described in detail in Sections 2.2 and 2.3), which can obtain the reflectance of different endmembers. Though the algorithm can be used to produce a synthetic reflectance product with both HSR and frequent coverage [16], it still has some shortcomings in theory. Firstly, solving the unmixing equations for the whole LSRI at once, the STDFM algorithm can only get one reflectance value for all HSR pixels belonging to one class in the unmixing of a LSRI [19], which does not take the spatial heterogeneity of the mean reflectance of an endmember into account.…”

mentioning

confidence: 99%

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An Enhanced Spatial and Temporal Data Fusion Model for Fusing Landsat and MODIS Surface Reflectance to Generate High Temporal Landsat-Like Data

et al. 2013

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Abstract:Remotely sensed data, with high spatial and temporal resolutions, can hardly be provided by only one sensor due to the tradeoff in sensor designs that balance spatial resolutions and temporal coverage. However, they are urgently needed for improving the ability of monitoring rapid landscape changes at fine scales (e.g., 30 m). One approach to acquire them is by fusing observations from sensors with different characteristics (e.g., Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS)). The existing data fusion algorithms, such as the Spatial and Temporal Data Fusion Model (STDFM), have achieved some significant progress in this field. This paper puts forward an Enhanced Spatial and Temporal Data Fusion Model (ESTDFM) based on the STDFM algorithm, by introducing a patch-based ISODATA classification method, the sliding window technology, and the temporal-weight concept. Time-series ETM+ and MODIS surface reflectance are used as test data for comparing the two algorithms. Results show that the prediction ability of the ESTDFM algorithm has been significantly improved, and is even more satisfactory in the near-infrared band

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Section: Evaluation Methodssupporting

confidence: 60%

Section: Point Spread Functionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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An Enhanced Spatial and Temporal Data Fusion Model for Fusing Landsat and MODIS Surface Reflectance to Generate High Temporal Landsat-Like Data

et al. 2013

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Data fusion in data scarce areas using a back-propagation artificial neural network model: a case study of the South China Sea

et al. 2017

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BI or IB: Which Better Generates High Spatiotemporal Resolution NDSI by Fusing Sentinel-2A/B and MODIS Data?

Dong,

Zhou,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Snow cover is a sensitive indicator of climate change. Normalized Difference Snow Index (NDSI) acquired from optical remote sensing data is usually used for monitoring snow cover, but the existing data are limited in spatiotemporal resolution. Here, we compared two blending strategies, Blendthen-Index (BI) and Index-then-Blend (IB), for generating high spatiotemporal resolution NDSI (daily, 20 m), and designed two groups of experiments (simulated and real) under three different snow cover periods over the Tibetan Plateau (TP). The Flexible Spatiotemporal DAta Fusion (FSDAF) model was used as the fusion model. MODIS (daily, 500 m) and Sentinel-2A/B (2-5 days, 20 m) data were used as the inputs. The accuracy of the fused NDSI was evaluated from both spectral (root mean square error [RMSE], correlation coefficient [R], and average difference [AD]) and spatial (Robert's edge [Edge], and local binary pattern [LBP]) dimensions. Our results showed that the IB strategy produced more accurate NDSI results, with lower RMSE, higher R, and AD closer to zero compared to the BI strategy. In addition, there was no obvious difference in terms of texture between the two fusion strategies. Generally, the IB strategy is a better choice for generating a high spatiotemporal resolution NDSI through the FSDAF model under different snow cover periods on the TP. This study can provide effective guidelines for producing better high-resolution NDSI time series on the TP.

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A model for spatial and temporal data fusion

Cited by 16 publications

References 8 publications

An Enhanced Spatial and Temporal Data Fusion Model for Fusing Landsat and MODIS Surface Reflectance to Generate High Temporal Landsat-Like Data

An Enhanced Spatial and Temporal Data Fusion Model for Fusing Landsat and MODIS Surface Reflectance to Generate High Temporal Landsat-Like Data

Data fusion in data scarce areas using a back-propagation artificial neural network model: a case study of the South China Sea

BI or IB: Which Better Generates High Spatiotemporal Resolution NDSI by Fusing Sentinel-2A/B and MODIS Data?

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