An Automatic Extraction Method of Built-up Area Based on Depth Feature and Graph Cut Method

Abstract

The invention discloses a depth feature and graph cut method-based built-up area automatic extraction method, and belongs to the technical field of image data processing. The method comprises the steps of using a high-resolution full-color image and a multispectral image subjected to ortho-rectification and linear cutting stretching processing as data sources; dividing the full-color image into image blocks same in size; extracting depth features of the image blocks by using a deep convolutional neural network; by taking the image blocks as nodes and taking the depth features as node features,building a graph model; determining built-up areas by using a graph cut method; performing voting by taking the image blocks as primitives based on multiple spectral indexes of the multispectral image to remove a false alarm; eliminating the built-up areas with the excessively small areas and non-built-up areas; performing super-pixel segmentation on the image blocks of built-up area edges; performing the voting based on the multiple spectral indexes of the multispectral image to remove the false alarm, thereby obtaining refined built-up area edges; and finally extracting an edge vector graph. The method can quickly, effectively and accurately realize the extraction of the built-up areas.

Description

Technical field [0001] The present invention belongs to the technical field of image data processing, and more specifically, relates to a method for automatically extracting built-up areas based on depth features and graph cutting methods. Background technique [0002] The built-up area refers to the area in the urban administrative area that has actually been developed and constructed, and the municipal public facilities and public facilities are basically equipped. For the core city, the built-up area includes a concentrated and contiguous part and several scattered areas that have been built. For remote sensing image visual identification tasks, built-up areas are areas where buildings are densely distributed and reach a certain area. There is currently no unified standard for the area of ​​built-up areas. [0003] Built-up area extraction is an important task in the interpretation of remote sensing image features. The built-up area extraction results have important uses in many...

AI Technical Summary

Problems solved by technology

[0006] A large number of features and classification methods have been applied in existing studies on the extraction of built-up areas from remote sensing images. These studies usually have the following three problems: First, the research uses a small amount of data, usually a small number of images in a certain area, covering The sample space of the model is narrow, the generalization ability of the model is poor, and it lacks practicability; secondly, the image is divided into built-up areas and non-built-up areas with pixels as primitives, which requires a huge amount of calculation, and for built-up areas without clear closed boundaries The effect of the region is poor; finally, the feature representation ability for built-up area extraction is insufficient, which is also the most important factor affecting the detection accuracy and scalability of the algorithm

[0007] Although the existing built-up area extraction algorithms have solved some problems to a certain extent, it is difficult to put them into practical use in remote sensing images of vast areas.

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