An urban detection method based on optimal integration of feature locations

Abstract

The invention provides an urban area detecting method based on feature position optimization and integration, prior learning is not needed, calculation is simple, and the urban area detecting method is more suitable for being implemented in practical application. The urban area detecting method includes the steps that step1, images are preprocessed, and the image processing process includes RGB color images conversion to gray level images and Gaussian pyramid generation; step2, urban position feature points are selected preliminarily; step3, the urban position feature points are screened; step4, regional integration is performed on urban areas based on Gaussian rendering weighting; step5, partition threshold values are obtained through a self-adaptive iteration method, binaryzation is performed on a weighting matrix, connected domains of binary images are marked, and the connected domains with the area smaller than the preset threshold value are rejected; step6, from the step2 to the step5 are repeated on all layers of a Gaussian pyramid generated in the step1, after results of all the layers are expanded to the size of the original images, a union set of the results is obtained to obtain an urban area candidate range, color features of the candidate range in the RGB color images, and pixel regions of which the color features do not meet the conditions are rejected to obtain a final detecting result.

Description

technical field [0001] The invention belongs to the technical field of image target detection, and in particular relates to a method for detecting an urban area based on optimal integration of feature positions. Background technique [0002] With the continuous development of remote sensing technology, the resolution of remote sensing images is getting higher and higher, and more and more information can be obtained. Among them, obtaining urban area information through remote sensing images has gradually become a research hotspot for scholars at home and abroad, which will bring great help to national construction and land exploration. First of all, as the first step of urban monitoring, urban area detection can effectively detect which areas belong to the city, which is helpful for the government or urban planning and construction departments to formulate urban construction and development plans; secondly, urban area detection can be used for urban change detection, Digita...

AI Technical Summary

Problems solved by technology

Among them, some scholars use the SIFT algorithm or the Harris corner point extraction algorithm to extract the feature points within the urban area, and then divide them into different subgraphs according to the distribution density of the feature points, and determine the urban area through multi-subgraph matching. This algorithm is theoretically feasible. However, the calculation is complex and the calculation time is long, which is not suitable for practical applications; some scholars obtain the density of buildings through image texture features, and segment urban areas based on density information

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