An improved spatio-temporal fusion model for remote sensing images based on singular spectrum analysis

doi:10.6046/zrzyyg.2022095

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Abstract

Spatio-temporal fusion can generate image sequences with sufficiently high temporal and spatial resolution. However, current studies tend to improve prediction accuracy using as much spatio-temporal data as possible and complex non-linear models, while few of them focus on analyzing images themselves by making full use of their intrinsic features, such as trends and textures. This study proposed a 2DSSA spatio-temporal fusion model (2DSSA-STFM) based on 2D singular spectrum analysis (2DSSA). In this model, the major spatial trends and details of the existing images at the target moment can be predicted by decomposing the images into trend and detail components. Firstly, the linear relationship between the trend of high-spatial-resolution data and low-spatial-resolution data was built to calculate the trend components of the images at the target moment. Then, the linear relationship between the low-resolution and the high-resolution detail components in two time phases was established to determine the detail components of the images at the target moment. Finally, the calculated trend and detail components were combined to form the target prediction images. The 2DSSA-STFM was applied to two sets of medium-resolution Landsat7 ETM+ and MODIS images, yielding smaller experimental errors than conventional spatio-temporal fusion models.

Keywords spatio-temporal fusion singular spectrum analysis trend component detail component decomposition

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TP753

Issue Date: 07 July 2023

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	Na AN
	Yingying ZHAO
	Yaqin SUN
	Aizhu ZHANG
	Hang FU
	Yanjuan YAO
	Genyun SUN

Cite this article:

Na AN,Yingying ZHAO,Yaqin SUN, et al. An improved spatio-temporal fusion model for remote sensing images based on singular spectrum analysis[J]. Remote Sensing for Natural Resources, 2023, 35(2): 89-96.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022095 OR https://www.gtzyyg.com/EN/Y2023/V35/I2/89

Fig.1 Algorithm flowchart

Fig.2 Images of MODIS and Landsat7 ETM+ of data set 1

Fig.3 Images of MODIS and Landsat7 ETM+ of data set 2

Fig.4 Trend and detail terms predicted by data set 1

Fig.5 Data set 1 prediction image

Tab.1 Quantitative evaluation results of data set 1

Fig.6 Data set 2 prediction image

Tab.2 Quantitative evaluation results of data set 2

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