Image data fusion indexing method based on FOV

Abstract

The invention relates to an FOV-based image data fusion indexing method, and the method comprises the steps: S100, obtaining image data based on a plurality of sensors, and building an image data set D; s200, acquiring a corresponding visual threshold of the image data in a geographic space based on optical parameters added to the image data, establishing a visual threshold set W corresponding to the D, and performing secondary fusion processing on the W to obtain a set W'and an image data set D 'corresponding to the W'; s300, obtaining a plurality of space elements based on a user, and classifying and marking the space elements; s400, based on W ', D' and spatial elements, establishing a multi-element spatial index constrained by geographic factors; the mining analysis efficiency of diversified elements such as time elements, space elements interested by the user, image elements and the like is improved, and unified and efficient management of massive and multivariate image data is realized.

Description

technical field [0001] The present application relates to the technical field of data processing, and in particular, to a FOV-based image data fusion indexing method. Background technique [0002] Various smart phones that can be seen everywhere and various cameras in the city generate a large amount of image data based on various sensors every day. development, etc. However, due to the massive amount of image data and the diversification of its storage format, resolution, size, etc., the efficient management of these image data faces huge challenges. However, traditional image data processing methods using intelligent technologies such as machine learning cannot efficiently manage massive and diverse image data due to the limitation of training models. [0003] The information disclosed in this Background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that ...

AI Technical Summary

Problems solved by technology

However, due to the massive amount of image data and the diversification of its storage format, resolution, size, etc., the efficient management of these image data is facing great challenges

However, the traditional image data processing methods using intelligent technologies such as machine learning cannot efficiently manage massive and diverse image data due to the limitations of the training model.

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