High-resolution remote sensing image search method fused with spatial relation semantics

Abstract

The invention discloses a high-resolution remote sensing image search method fused with spatial relation semantics and the method comprises two parts, namely the off-line treatment of remote sensing image and the on-line search of the remote sensing image. In the off-line treatment part, the visual features of the remote sensing image is firstly extracted and the visual feature, spatial object semantic and spatial relation semantic features are stored in relational database. In the on-line search part, the searching is performed according to the object semantic feature of the image to obtain a rough search result; then a template image is selected from the rough search result, further searching is performed to the rough search result according to the visual feature of the template image and the spatial relation semantic feature to return to the visual feature and the spatial semantic feature and assemble with the similar images of the selected template image, and the searching process is completed. As the method comprehensively uses the visual feature of image and the spatial object semantic and spatial relation semantic features, higher search precision can be obtained.

Description

technical field [0001] The invention relates to a method for querying and retrieving high-spatial-resolution remote sensing images (hereinafter referred to as high-resolution remote sensing images), specifically a method for retrieving high-resolution remote sensing images that integrates spatial relationship semantics and image visual features, and belongs to Remote sensing image processing and information extraction. technical background [0002] Remote sensing image retrieval (or called remote sensing image query) is the process of finding the image or image sequence that the user is interested in from the remote sensing image database. With the rapid increase of remote sensing image data, how to effectively manage the huge image database and quickly and accurately query and retrieve image information has become an urgent problem to be solved. Summarizing the current research progress, there are mainly three methods for remote sensing image retrieval: [0003] (1) Text-...

AI Technical Summary

Problems solved by technology

This kind of method mainly has the following problems: 1) the workload of manual annotation is too large; 2) manual annotation is subjectivity and uncertainty

This method of expressing the spatial relationship of objects can achieve good retrieval results for conventional images (common multimedia, medical images, etc.) with a single background and a small number of objects, but it is not suitable for remote sensing images.

This is because remote sensing images, compared with ordinary multimedia and medical images, have various types of ground features, very complex distributions, and very complex combinations of spatial relationships between them, which are difficult to describe clearly with the above quadruple method

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