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

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

doi:10.6046/zrzyyg.2022095

2023 Vol. 35, No. 2

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AN Na, ZHAO Yingying, SUN Yaqin, ZHANG Aizhu, FU Hang, YAO Yanjuan, SUN Genyun. 2023. An improved spatio-temporal fusion model for remote sensing images based on singular spectrum analysis. Remote Sensing for Natural Resources, 35(2): 89-96. doi: 10.6046/zrzyyg.2022095

Citation:

AN Na, ZHAO Yingying, SUN Yaqin, ZHANG Aizhu, FU Hang, YAO Yanjuan, SUN Genyun. 2023. An improved spatio-temporal fusion model for remote sensing images based on singular spectrum analysis. Remote Sensing for Natural Resources, 35(2): 89-96. doi: 10.6046/zrzyyg.2022095

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

1. China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
;2. Changsha Planning and Design Survey Research Institute, Changsha 410007, China
;3. College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
;4. Satellite Environment Center, Ministry of Environmental protection of China, Beijing 100094, China
;5. Laboratory for Marine Resources Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China

More Information

Corresponding author: YAO Yanjuan

Received Date: 21 March 2022

Revised Date: 09 September 2022

Publish Date: 07 July 2023

Abstract

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.
- spatio-temporal fusion /
- singular spectrum analysis /
- trend component /
- detail component /
- decomposition