Road Extraction Algorithm for Remote Sensing Images based on Multi Ant Colony Dynamic Collaborative Optimization

Remote sensing image road extraction algorithms play an important role in the fields of geographic information systems and intelligent transportation. However, traditional road extraction algorithms still have problems such as low accuracy and long algorithm response time in information extraction. This article aims to use multi ant colony dynamic collaboration optimization to design remote sensing image road extraction algorithms, in order to better meet the needs of information extraction. Firstly, the remote sensing images (RSI) were preprocessed through radiometric calibration and atmospheric correction; then, multi ant colony collaborative optimization was used to extract image features; afterwards, the maximum minimum ant system was used to extract roads; finally, to verify the application effect of multi ant colony collaborative optimization in remote sensing image road extraction algorithms, it was compared with traditional algorithms. Through experiments, it was found that the average accuracy of the image extraction algorithm in this article can reach 91.78%, and the average response time of the algorithm is about 0.017 milliseconds. The average accuracy of traditional algorithm image extraction is about 78.86%, and the average response time of the algorithm is about 0.075 milliseconds. Research has shown that the algorithm proposed in this paper has higher accuracy and better response speed in road extraction from RSI. The study emphasizes the importance of multi ant colony dynamic collaborative optimization in remote sensing image road extraction algorithms, providing more effective methods to promote information extraction.