A Road Extraction Method Based on Prediction and Residual Refinement Network

A predictive network and road extraction technology, applied in neural learning methods, biological neural network models, character and pattern recognition, etc., to achieve the effect of improving clarity, reducing information loss, and reducing information loss

Active Publication Date: 2022-07-05
HUBEI UNIV OF TECH
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Problems solved by technology

Due to the variable road types in aerial images or high-resolution remote sensing images, road extraction still faces severe challenges

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  • A Road Extraction Method Based on Prediction and Residual Refinement Network
  • A Road Extraction Method Based on Prediction and Residual Refinement Network
  • A Road Extraction Method Based on Prediction and Residual Refinement Network

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[0019] In order to facilitate the understanding and implementation of the present invention by those skilled in the art, the present invention will be described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the embodiments described herein are only used to illustrate and explain the present invention, but not to limit it. this invention.

[0020] see figure 1 , the present invention provides a kind of road extraction method based on prediction and residual refinement network, comprising the following steps:

[0021] Step 1: Design the aerial image road extraction network combining the prediction network and the residual refinement network;

[0022] In this embodiment, the prediction network adopts the Encoder-Decoder structure, combined with the hole convolution module DCM and the multi-layer pooling module MPM;

[0023] Recently, the U-Net algorithm has achieved very good results in the field of cell segmen...

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Abstract

The invention discloses a road extraction method based on prediction and residual refinement network, and designs an aerial image road extraction algorithm combining prediction network and residual refinement network according to different characteristics of road targets. The prediction network adopts the Encoder-Decoder structure; secondly, the prediction network combines the atrous convolution module DCM and the multi-core pooling module MPM, which can fully obtain the context information and road edge information of the image and improve the road edge detection ability. The residual refinement network will refine the initial road prediction results generated by the prediction network and improve the road fuzzification caused by noise in the prediction network. The network also integrates BCE, SSIM and IoU loss functions for supervised training to reduce road information loss. It is beneficial to extract the complete road structure.

Description

technical field [0001] The invention belongs to the technical fields of digital image processing, pattern recognition and machine learning, and relates to a road extraction method, in particular to a road extraction method based on prediction and residual refinement network. Background technique [0002] Road extraction can be formulated as a binary classification problem, similar to multi-object segmentation in semantic segmentation, where road segmentation reduces the segmentation categories in comparison. Due to the variable road types in aerial images or high-resolution remote sensing images, road extraction still faces severe challenges. After years of research, a large number of algorithms have emerged for road extraction from aerial images, which can be roughly divided into three categories: feature-based, object-based, and knowledge-based road extraction methods. The road extraction algorithm based on feature level includes template matching method, edge and paralle...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V20/56G06V20/58G06N3/08
CPCG06N3/08G06V20/588G06V20/56Y02T10/40
Inventor 熊炜管来福李敏王娟李利荣曾春艳刘敏
Owner HUBEI UNIV OF TECH
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