A method, system and device for spatial-temporal fusion of remote sensing image data

Abstract

The present invention proposes a method, system and equipment for spatio-temporal fusion of remote sensing image data. Change detection images are obtained by computing low-resolution remote sensing images in two time phases; the edge area of ​​the high-resolution image in the first phase is extracted, and each The abundance corresponding to the number of high-resolution pixels; calculate the time-phase change value of various pixels according to the extraction results and abundance of the edge area; calculate the time prediction value and spatial prediction value; according to the degree of surface homogeneity, time prediction Values ​​and spatial prediction values, combined with neighborhood information to assign residual values ​​to obtain a preliminary fusion image; use the established optimization model to correct the changing pixels contained in the preliminary fusion image, and obtain the spatio-temporal data fusion result. The method described in this embodiment comprehensively considers the applicability of different change detection algorithms in different scenarios, improves the overall spectral accuracy of fusion and retains more spatial detail information, and can obtain better spatio-temporal data fusion results.

Description

technical field [0001] The invention relates to the technical field of remote sensing, in particular to a method, system and equipment for spatio-temporal fusion of remote sensing image data. Background technique [0002] Obtaining high temporal and spatial resolution remote sensing images is of great significance to intensive time series surface observation research. Existing spatio-temporal fusion methods have their own advantages and problems, among which there are two typical problems: [0003] 1) Most of the existing spatio-temporal fusion algorithms are based on the assumption that the types of ground objects do not change between two periods, so most of the algorithms often fail in the face of changes in the types of ground objects, and the lack of a judgment mechanism for changes in the types of ground objects limits spatio-temporal fusion The application scenarios of the algorithm cannot judge the reliability of the fused image in practical applications. [0004] ...

AI Technical Summary

Problems solved by technology

[0003] 1) Most of the existing spatio-temporal fusion algorithms are based on the assumption that the types of ground objects do not change between two periods of time. Therefore, most algorithms often fail in the face of changes in the types of ground objects. The lack of a judgment mechanism for changes in the types of ground objects limits space-time fusion. The application scenarios of the algorithm, the reliability of the fused image cannot be judged in practical applications

[0004] 2) The current space-time fusion algorithm faces three different types of surface landscapes: heterogeneous, abrupt, and homogeneous. The results obtained by using different space-time fusion algorithms are very different, which cannot guarantee robustness and reliability. Accurately predict remote sensing images in all scenarios

Images