Remote sensing image road extracting method based on stereo constraint

Abstract

The invention relates to a remote sensing image road extracting method based on stereo constraint, which comprises the following steps: extracting and refining the edge of the left image of a stereo pair, thereby obtaining a binary image of the single pixel edge; carrying out shape constraint and vectorization on the binary image of the single pixel edge by using an improved phase grouping method; carrying out edge line node matching on the basis of grey scale correlations, thereby determining the homonymous image point of the edge line node on the right image; resolving node elevation on the basis of an image pair RPC model parameter or interior / exterior orientation element forward intersection method; and determining the road extraction result on the basis of the road edge line constrained by the node elevation. Starting with the stereo image, the invention extracts the elevation information of the road edge line, which is obtained after edge extraction, refining, shape constraint and vectorization, and establishes a model by which the branch nodes of the candidate road edge lines can be matched and the elevation information can be acquired by carrying out forward intersection on the homonymous point information; and according to the elevation characteristic of the road, the invention establishes elevation constraint conditions, carries out elevation constraint, and extracts satisfactory road edge lines from the candidate road edge lines, thereby realizing quick automatic extraction of road thematic information on the remote sensing image.

Description

technical field [0001] The invention belongs to the technical field of remote sensing, and in particular relates to a method for extracting roads from remote sensing images based on three-dimensional constraints. Background technique [0002] Road is a kind of important thematic information, which is of great value to geoscience analysis and urban management. The day-to-day management of cities is of great value. Using remote sensing images to extract road information is a fast and up-to-date way. Therefore, it has become a research hotspot. At present, the methods of extracting roads from remote sensing images include manual methods, semi-automatic tracking methods and automatic pattern classification methods. Among them, the manual method includes the extraction directly on a single remote sensing image, and the extraction method based on stereo observation under a stereo image pair (mostly an aerial image pair); semi-automatic tracking mainly uses a given seed point on...

AI Technical Summary

Problems solved by technology

It is characterized by the refinement of classification, but it relies heavily on the classification protocol (it is better to have protocol library support), and the efficiency of road information extraction is not high, requiring a lot of interaction, especially in urban areas

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