Method for predicting traffic flow extracted by improved C-V model-based remote sensing image road network

Abstract

The invention discloses a method for predicting traffic flow extracted by an improved C-V model-based remote sensing image road network, which comprises the following steps of: (1) preprocessing an original remote sensing image; (2) selecting a seed point and segmenting a first road network sub-image; (3) extracting a road network area by an improved C-V model-based level set method; (4) extracting a road network central line by a morphological thinning method; (5) segmenting a next road network sub-image by using a sub-image position decision rule and automatically acquiring a road network initial curve in the road network sub-images by a threshold segmentation and morphological method; (6) vectorizing a road network; and (7) predicting the traffic flow. By integrating technology such as remote sensing, geographic information system (GIS), image identification, traffic planning and the like, an urban road network can be more accurately, efficiently and cheaply updated in real time, the traffic flow prediction cost is lower, the traffic flow prediction accuracy is higher and the traffic flow prediction period is shorter, so that decision makers can be effectively assisted in making traffic planning decisions.

Description

Technical field [0001] The present invention relates to the three major fields of high-resolution satellite remote sensing image processing and information extraction application, geographic information system, and traffic planning, and specifically relates to the use of image recognition technology to achieve road network extraction, traffic prediction technology to achieve traffic flow prediction, and geographic information system Realize thematic map production such as map management and flow. 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, traffic congestion in many cities in my country and the resulting social problems such as traffic accidents and traffic environmenta...

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.

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