High-resolution remote sensing image building extraction method

Abstract

The invention discloses a high-resolution remote sensing image building extraction method. Firstly, candidate object sets are extracted by combining technologies such as image segmentation and shadowdetection; secondly, based on a scale parameter self-adaptive extraction strategy, a differential attribute profile is constructed under object boundary constraints, and an initial building set is obtained; and finally, uncertain objects in the set are automatically extracted according to the inter-class separability, and secondary judgment is carried out to obtain a final building extraction result. According to the invention, the performance of remote sensing image building extraction in urban scenes with numerous ground object types is improved.

Description

technical field [0001] The invention belongs to the field of remote sensing images, in particular to a method for extracting buildings from remote sensing images. Background technique [0002] With the successful operation and continuous improvement of various high-resolution remote sensing platforms, high-resolution remote sensing images have been widely used in many fields, including geographic data update, urban thematic map production, and multi-angle urban classification analysis. Among them, the semantic labeling of urban areas based on high-resolution remote sensing images, especially the automatic recognition and extraction of buildings usually plays an important role in these practical applications, and has always been one of the research hotspots in the field of remote sensing. [0003] Compared with traditional mid- and low-resolution remote sensing images, high-resolution remote sensing images contain richer building detail information, clearer outlines and struc...

AI Technical Summary

Problems solved by technology

[0006] (1) The extraction of connected regions only depends on the similarity of specific attributes (area, shape, etc.) between adjacent pixels. Since it is not restricted by the inherent boundaries of buildings, connected regions may contain adjacent features with similar attributes pixels, thereby reducing the accuracy of the extracted MAPs;

[0007] (2) The setting of the scale parameters of MAPs should match the attribute characteristics of typical buildings in the image, and manual setting according to experience not only reduces the degree of automation of the method, but also may fall into local optimum;

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