Sub-pixel positioning method and system based on spatial gravity and pixel aggregation degree

Abstract

The invention provides a sub-pixel positioning method and system based on spatial gravity and pixel aggregation degree. The method comprises the steps of S1, based on abundance values of various ground objects in mixed pixels of a remote sensing image, obtaining a total gravity value of sub-pixels belonging to the various ground objects in central mixed pixels and sub-pixels of the same ground object types in 8-neighborhood adjacent mixed pixels, and based on the calculated gravity value, allocating initialized ground object types to the sub-pixels in the central mixed pixels; and S2, carryingout pixel aggregation degree optimization on sub-pixel distribution in the step S1: S21, calculating a pixel aggregation degree objective function; S22, exchanging the pixels to optimize sub-pixel spatial distribution and updating the objective function in the step S21; S23, traversing all the mixed pixels in the remote sensing image, and executing the step S22 for each mixed pixel; and S24, by using an iteration algorithm, repeating the steps S22-S23. A sub-pixel positioning result calculated by the method and the system has not only relatively high spatial correlation but also effective ground object spatial distribution, so that the applicability of a sub-pixel positioning model to the ground objects with different sizes is improved.

Description

technical field [0001] The present invention relates to remote sensing technology, and more specifically, to a sub-pixel positioning method. Background technique [0002] The common phenomenon of mixed pixels in remote sensing images has become one of the main obstacles to further improve the accuracy of object recognition and remote sensing classification. For the decomposition of mixed pixels, it is necessary to study the properties, abundance and spatial distribution of the pixels in the mixed pixels. The corresponding technologies are endmember extraction technology, mixed pixel decomposition technology and sub-pixel positioning technology. Judging from the existing research, there are many and in-depth studies on the first two technologies. However, only relying on endmember extraction technology and mixed pixel decomposition technology can only obtain the proportion information of each endmember in the mixed pixel, but cannot obtain the distribution of various ground ...

AI Technical Summary

Problems solved by technology

[0004] However, most of the above sub-pixel localization models can only target one of the two sizes of ground objects, and few models can take into account the coexistence of both

For example, the sub-pixel positioning model based on spatial correlation is mainly aimed at the situation where the object size is larger than the pixel resolution. Plaques are easily overlooked, which affects the sub-pixel positioning effect and mapping quality

The sub-pixel localization model based on spatial pattern fitting is mainly aimed at the situation where the size of ground objects is smaller than the pixel resolution, and relies too much on the training image structure information, while ignoring the spatial correlation between experimental image pixels / sub-pixels, thus Unable to obtain high sub-pixel positioning accuracy

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