Spatial-temporal fraction map fusion with multi-scale remotely sensed images

Zhang, Yihang; Foody, Giles M.; Ling, Feng; Li, Xiaodong; Ge, Yong; Du, Youwei; Atkinson, Peter M.

doi:10.1016/j.rse.2018.05.010

Cited by 35 publications

(13 citation statements)

References 60 publications

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“…Similar to Zhu et al [12], Zhang et al [54] developed a method for fusing coarse-spatial, fine-temporal and fine-spatial, coarse-temporal images that assumes that surface reflectance values of coarse resolution pixels are mixed. The method relies on predicting the fraction map of fine resolution images from the available coarse resolution fraction maps by making use of images acquired before and after the prediction dates.…”

Section: Unmixing-based Spatiotemporal Image Fusion Methodsmentioning

confidence: 99%

Spatiotemporal Image Fusion in Remote Sensing

2019

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In this paper, we discuss spatiotemporal data fusion methods in remote sensing. These methods fuse temporally sparse fine-resolution images with temporally dense coarse-resolution images. This review reveals that existing spatiotemporal data fusion methods are mainly dedicated to blending optical images. There is a limited number of studies focusing on fusing microwave data, or on fusing microwave and optical images in order to address the problem of gaps in the optical data caused by the presence of clouds. Therefore, future efforts are required to develop spatiotemporal data fusion methods flexible enough to accomplish different data fusion tasks under different environmental conditions and using different sensors data as input. The review shows that additional investigations are required to account for temporal changes occurring during the observation period when predicting spectral reflectance values at a fine scale in space and time. More sophisticated machine learning methods such as convolutional neural network (CNN) represent a promising solution for spatiotemporal fusion, especially due to their capability to fuse images with different spectral values.

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Section: Unmixing-based Spatiotemporal Image Fusion Methodsmentioning

confidence: 99%

Spatiotemporal Image Fusion in Remote Sensing

2019

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“…The hybrid color mapping method uses hybrid color mapping to establish the relation between coarse images from different times, and then the relation is utilized on the fine image to obtain the final prediction [43]. The spatialtemporal fraction map fusion model first generates fine resolution fraction change maps by using kernel ridge regression, and then uses a temporal-weighted fusion model to obtain a fine resolution fraction map of the predicted date [44]. Fit-FC uses three models, i.e., regression fitting, spatial filtering and residual compensation to conduct the STF of between Sentinel-2 and Sentinel-3 images [45].…”

Section: Weight Function-based Methodsmentioning

confidence: 99%

Spatio-temporal fusion for remote sensing data: an overview and new benchmark

et al. 2020

Sci. China Inf. Sci.

104

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Spatio-temporal fusion (STF) aims at fusing (temporally dense) coarse resolution images and (temporally sparse) fine resolution images to generate image series with adequate temporal and spatial resolution. In the last decade, STF has drawn a lot of attention and many STF methods have been developed. However, to date the STF domain still lacks benchmark datasets, which is a pressing issue that needs to be addressed in order to foster the development of this field. In this review, we provide (for the first time in the literature) a robust benchmark STF dataset that includes three important characteristics: (1) diversity of regions, (2) long timespan, and (3) challenging scenarios. We also provide a survey of highly representative STF techniques, along with a detailed quantitative and qualitative comparison of their performance with our newly presented benchmark dataset. The proposed dataset is public and available online.

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“…Because STDGFM does not explicitly account for the mixed pixel effect, specific analytical procedures should be incorporated into STDGFM to address heterogeneous pixel problems. Class fraction or composition of a coarse resolution pixel can be first computed using spectral unmixing or existing land-cover maps [42,43]. This sub-pixel fraction information can then be used as a constraint to estimate the optimized deconvolution matrix.…”

Section: Further Improvement Of Stgdfmmentioning

confidence: 99%

A Cross-Resolution, Spatiotemporal Geostatistical Fusion Model for Combining Satellite Image Time-Series of Different Spatial and Temporal Resolutions

2020

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Dense time-series with coarse spatial resolution (DTCS) and sparse time-series with fine spatial resolution (STFS) data often provide complementary information. To make full use of this complementarity, this paper presents a novel spatiotemporal fusion model, the spatial time-series geostatistical deconvolution/fusion model (STGDFM), to generate synthesized dense time-series with fine spatial resolution (DTFS) data. Attributes from the DTCS and STFS data are decomposed into trend and residual components, and the spatiotemporal distributions of these components are predicted through novel schemes. The novelty of STGDFM lies in its ability to (1) consider temporal trend information using land-cover-specific temporal profiles from an entire DTCS dataset, (2) reflect local details of the STFS data using resolution matrix representation, and (3) use residual correction to account for temporary variations or abrupt changes that cannot be modeled from the trend components. The potential of STGDFM is evaluated by conducting extensive experiments that focus on different environments; spatially degraded datasets and real Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat images are employed. The prediction performance of STGDFM is compared with those of a spatial and temporal adaptive reflectance fusion model (STARFM) and an enhanced STARFM (ESTARFM). Experimental results indicate that STGDFM delivers the best prediction performance with respect to prediction errors and preservation of spatial structures as it captures temporal change information on the prediction date. The superiority of STGDFM is significant when the difference between pair dates and prediction dates increases. These results indicate that STGDFM can be effectively applied to predict DTFS data that are essential for various environmental monitoring tasks.

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Spatial-temporal fraction map fusion with multi-scale remotely sensed images

Abstract: with fine-spatial-temporal-resolution that can support studies of land cover dynamics at the sub-pixel scale.

Cited by 35 publications

References 60 publications

Spatiotemporal Image Fusion in Remote Sensing

Spatiotemporal Image Fusion in Remote Sensing

Spatio-temporal fusion for remote sensing data: an overview and new benchmark

A Cross-Resolution, Spatiotemporal Geostatistical Fusion Model for Combining Satellite Image Time-Series of Different Spatial and Temporal Resolutions

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