Unmanned aerial vehicle image building roof extraction method based on full convolutional neural network

A convolutional neural network and extraction method technology, applied in the field of UAV image building roof extraction, can solve problems such as difficult models, underutilized buildings, and high dependence on prior knowledge

Active Publication Date: 2019-12-06
云南省水利水电勘测设计院
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Problems solved by technology

Although the current number-driven method has better results in comparison, it still does not make full use of the building features and has poor robustness.
Based on the model-driven method, there are various types of build

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  • Unmanned aerial vehicle image building roof extraction method based on full convolutional neural network
  • Unmanned aerial vehicle image building roof extraction method based on full convolutional neural network
  • Unmanned aerial vehicle image building roof extraction method based on full convolutional neural network

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[0051] The present invention proposes a method for extracting building roofs from UAV images based on a fully convolutional neural network. The method aims at rotating, blurring, and gamma transforming the samples for the UAV image building roof sample library to expand the number of samples. Increase the robustness of deep learning networks. Firstly, the convolutional neural network based on layer-skip connections is used to extract the features of the roof of the building, and the feature map of the building roof obtained by the convolutional neural network is reconstructed by deconvolution. Then, the trained network model is used to detect the roof of the building, and the edge of the detection result is refined by using the conditional random field. Finally, the D-S evidence theory is used to reason and verify the extraction result of the building roof, and the false detection object is eliminated.

[0052] Below in conjunction with accompanying drawing, describe technical...

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Abstract

The invention discloses an unmanned aerial vehicle image building roof extraction method based on a full convolutional neural network. The method comprises the following steps: in a first part, establishing an aerial image building roof sample library; in the second part, designing a full convolutional neural network to carry out feature learning on a building roof sample; performing building roofdetection by using the trained network, and obtaining a more accurate building roof result through post-processing of an extraction result in a third part. The method is different from a traditionalextraction method, and makes full use of rich unmanned aerial vehicle image resources in the aspect of data acquisition. In an algorithm design aspect, a specific full convolutional neural network based on skip layer connection is designed, gradient diffusion and gradient explosion are prevented while building roof features are fully extracted. In the aspect of post-processing, a conditional random field and a D-S evidence theory are utilized to carry out building roof extraction result post-processing, and the extraction precision of the unmanned aerial vehicle image building roof is improvedthrough post-processing.

Description

technical field [0001] The invention relates to the technical field of UAV image processing, in particular to a method for extracting building roofs from UAV images based on a fully convolutional neural network. Background technique [0002] With the advancement of my country's urbanization process and the rapid development of economic construction, the automatic extraction of buildings has become more and more important for the public and various industries. The rapid extraction and update of building elements has become the basic geographic information construction of our country. a very important content. At present, the comprehensive internal adjustment based on high-resolution remote sensing images is the main means of updating basic geographic information elements. Compared with remote sensing images for building interior judgment, UAV image acquisition is less difficult, data production is more flexible, and is less restricted by external conditions. The advantages of...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06N3/04G06N3/08
CPCG06N3/08G06V20/176G06V10/462G06N3/045Y02T10/40
Inventor 于洋刘斌苏正猛白少云吴波涛王建春梅伟张永利王静顾世祥黄俊伟冯琦白世晗
Owner 云南省水利水电勘测设计院
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