Space-time fusion method, system and device for remote sensing image data

Abstract

The invention provides a remote sensing image data space-time fusion method, system and device. The method includes obtaining a change detection imagethrough calculation of two time-phase low-resolution remote sensing images; extracting an edge region of the high-resolution image of the first time phase, and calculating abundance corresponding to the number of various high-resolution pixels; calculating time-phase change values of various pixels according to the extraction result and abundance of the edge region; calculating a time prediction value and a space prediction value; according to the earth surface homogeneity degree, the time prediction value and the space prediction value, combining neighborhood information to distribute a residual value so as to acquire a preliminary fusion image; and utilizing the established optimization model to correct the change pixels contained in the preliminary fusion image to obtain a spatio-temporal data fusion result. According to the method provided by the embodiment of the invention, the applicability of different change detection algorithms in different scenes is comprehensively considered, the overall spectral precision of fusion is improved, more spatial detail information is reserved, and a better spatio-temporal data fusion result can be obtained.

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

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