Method for automatically extracting town built-up areas by using town texture feature density

Abstract

The invention discloses a method for automatically extracting town built-up areas by using town texture feature density, belongs to the field of remote sensing image information extraction, and aims at high-resolution remote sensing data to construct town texture feature density of remote sensing images by using a multi-scale simple linear iteration algorithm so as to automatically extract the town built-up areas. The method comprises the following steps: firstly, sensitivity of different texture features of the gray level co-occurrence matrix in town extraction is analyzed through utilizing J-M distance, and the texture difference features serve as the classification basis; and then, multi-scale superpixel segmentation is carried out by utilizing a simple linear iterative clustering algorithm (SLIC), a feature density image after multiple times of segmentation is calculated by taking superpixels as a range for calculating local feature density, and superposition is carried out to construct a feature density image of the to-be-classified image. The extraction result of the method has good integrity rate and accuracy, and particularly in the aspect of integrity, the coincidence degree of the town edge extracted through the method and the actual edge is extremely high, and the manual detection effect can be achieved.

Description

technical field [0001] The invention belongs to the method for extracting remote sensing image information, and specifically relates to a method for high-resolution remote sensing images (10m and higher resolution), based on multi-layer superpixel segmentation, constructing town texture feature density, and realizing automatic extraction of images Approach to urban built-up areas. Background technique [0002] Urban information extraction based on remote sensing images refers to identifying and marking urban built-up areas in remote sensing satellite images, aerial images or images taken by UAVs. The scope, distribution, and changes of urban built-up areas are of great significance to urban planning, land use resource allocation, and environmental monitoring. At the same time, they are also the data basis for many research directions and have attracted the attention of a large number of researchers. However, complex ground information and diverse urban settlements make the ...

AI Technical Summary

Problems solved by technology

However, the acquisition cost of high-resolution data is high, which is not conducive to the extraction of large-scale urban built-up areas.

In addition, the existing methods rely too much on the quality of the data itself and the accuracy of the extracted features. However, the phenomenon of "different objects with the same spectrum" in remote sensing images is common, and there are a large number of natural features similar to urban textures, resulting in a large number of false extractions.

[0004] The remote sensing satellite data with a spatial resolution of 10 meters not only has clear texture information, but also has smoother texture information inside the urban built-up area, and the data can be obtained for free, but so far there are few applications in urban extraction

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