Remote-sensing image road network extract method based on improved C-V model

Abstract

The invention discloses a remote-sensing image road network extract method based on an improved C-V mode, comprising the following steps: (1) pre-processing initial remote-sensing images; (2) selecting seed points and segmenting a first road network subimage; (3) using the level set method of the improved C-V model to extract a road network area; (4) using the method of morphologic detailing to extract the center line of the road network; (5) segmenting the next road network subimage according to the subimage position criterion rule to automatically obtain the road network initial curve according to threshold segmentation and the morphologic method. The method of the invention can be employed to effectively extract the road network from the remote-sensing image, upgrade urban road network in real time and effectively assist decision-makers in formulating traffic planning.

Description

technical field [0001] The invention relates to a method for extracting a road network from a remote sensing map based on a C-V model, and belongs to the application field of high-resolution satellite remote sensing image processing and information extraction. Background technique [0002] With the rapid development of my country's social economy, the process of urbanization continues to accelerate, the urban population and the number of motor vehicles continue to increase, and the contradiction between transportation demand and urban road conditions and capabilities has become increasingly prominent. At present, the phenomenon of traffic congestion in many cities in our country and the social problems such as traffic accidents and traffic environmental pollution caused by it have been extremely serious. As the lifeblood of the city, traffic has become the bottleneck of urban development, seriously restricting the development of society and economy, and affecting the daily w...

AI Technical Summary

Problems solved by technology

However, the traditional level set C-V model has the following deficiencies: First, it needs to correct the initial curve in iterations, that is, re-initialize the signed distance function, which increases the amount of calculation, reduces the segmentation speed, and is difficult to guarantee the segmentation accuracy; The second is that the division of the region only considers the grayscale characteristics of the RGB space of the image, which loses the rich color information of the color image. Because the phenomenon of foreign objects with the same grayscale value is common, it will inevitably cause the wrong segmentation of the target.

At the same time, the similarity in the color of background objects also seriously affects the recognition effect of the road network.

Therefore, the traditional level set C-V model has deficiencies in image segmentation speed, efficiency, and accuracy, so that the traditional C-V model-based remote sensing image road network extraction method also has corresponding deficiencies in speed, efficiency, and accuracy.

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