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Remote sensing image building accurate segmentation method

A technology of remote sensing images and buildings, applied in image analysis, neural learning methods, image enhancement, etc., can solve problems such as difficulty in capturing image details and inaccurate identification of building edge areas

Active Publication Date: 2021-05-28
CHONGQING GEOMATICS & REMOTE SENSING CENT +1
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  • Application Information

AI Technical Summary

Problems solved by technology

Due to the limitation of resolution, this method is usually difficult to capture the detailed information of the image, resulting in inaccurate recognition of the edge area of ​​the building

Method used

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  • Remote sensing image building accurate segmentation method
  • Remote sensing image building accurate segmentation method
  • Remote sensing image building accurate segmentation method

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Embodiment Construction

[0039] The specific implementation manner and working principle of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0040] Such as figure 1 As shown, a method for accurately segmenting buildings in remote sensing images, the specific steps are as follows:

[0041] Step 1: Build a building extraction network including feature extraction module, dilated convolution module, attention module, upsampling module and convolution prediction module, such as figure 2 shown, where:

[0042] The feature extraction module is used to perform multi-scale feature extraction on the input remote sensing image to obtain a multi-scale feature map;

[0043] Specifically, the specific process of the feature extraction module performing multi-scale feature extraction on the input remote sensing image is as follows:

[0044] The pre-trained residual network without the fully connected layer in the building extraction network is used ...

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Abstract

The invention discloses a remote sensing image building accurate segmentation method. The method comprises the following steps: constructing a building extraction network comprising a feature extraction module, a cavity convolution module, an attention module, an up-sampling module and a convolution prediction module; based on the training sample set, adopting a multi-scale composite loss function combining Dice Loss and BCE Loss to train the constructed building extraction network; and inputting a remote sensing image to be extracted into the trained building extraction network to obtain a building extraction result. The method has the remarkable effects that the feature learning and generalization ability is high; the network is low in complexity and easy to train; and the building extraction precision is high.

Description

technical field [0001] The invention relates to the technical field of automatic extraction of remote sensing image information, in particular to a method for accurately segmenting remote sensing image buildings. Background technique [0002] Building extraction is a classic problem in the field of automatic extraction of remote sensing image information. Its main purpose is to identify and extract building areas in remote sensing images. The results of building extraction are widely used in many fields, such as military reconnaissance, environmental protection, cartography and geographic analysis, etc. Therefore, building extraction has important research value. [0003] Most of the traditional methods use recognition algorithms based on the linear features of building edges. This type of method has the advantages of simplicity and high efficiency, but has defects such as low recognition rate and many errors. Such methods often cause misjudgment of building shadows, and l...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/11G06K9/46G06N3/04G06N3/08
CPCG06T7/0002G06T7/11G06N3/08G06T2207/10032G06T2207/20016G06T2207/20081G06T2207/20084G06T2207/30184G06V10/44G06N3/048G06N3/045
Inventor 丁忆张泽烈李朋龙马泽忠吴目宇胡翔云胡艳肖禾张觅李晓龙王亚林林熙焦欢黄潇莹彭婧
Owner CHONGQING GEOMATICS & REMOTE SENSING CENT
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