Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

SAR image road extraction method and device based on semantic segmentation and conditional random field

A conditional random field and semantic segmentation technology, applied in the computer field, can solve problems such as the reduction of feature receptive field, the loss of image details, and the segmentation effect does not increase but decreases, so as to improve the loss of image information, improve the extraction performance, and optimize the segmentation results. Effect

Active Publication Date: 2021-06-22
NAT UNIV OF DEFENSE TECH
View PDF4 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, in Mode 1, prediction is made on image slices to avoid the loss of information caused by downsampling, but the network cannot extract semantic information outside the slices, resulting in a greatly reduced feature receptive field. Due to the complex structure of SAR airport images and The surrounding interference is serious. This mode is prone to regional judgment errors, resulting in a decrease in the segmentation effect instead of an increase. In addition, this mode takes a long time and has little application value.
In mode 2, since the image is first down-sampled, and the feature size is reduced in order to extract high-dimensional features during the forward pass of the network, the feature map obtained by this method can obtain a large receptive field feature, but the image details are seriously lost.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • SAR image road extraction method and device based on semantic segmentation and conditional random field
  • SAR image road extraction method and device based on semantic segmentation and conditional random field
  • SAR image road extraction method and device based on semantic segmentation and conditional random field

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0045] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

[0046] In one of the examples, as figure 1 所示,提供一种基于语义分割和条件随机场的SAR图像道路提取方法,具体步骤如下:

[0047] 步骤102,获取SAR道路图像样本。

[0048] SAR道路图像样本指的是已标注的SAR道路图像,例如:以SAR道路图像和对应的图像真值作为SAR道路图像样本。

[0049] 道路可以是建筑物之间的道路,也可以是机场跑道等,再次不做具体的限制。

[0050] 以高分系列卫星获取的机场道路SAR道路预测概率图进行标注为例,机场道路SAR道路预测概率图中机场道路标注为类别“1”,对应的像素值为“255”,其余背景为类别“0”,对应的像素值为0,从而得到机场道路SAR道路预测概率图的真值。

[0051] 步骤104,将SAR道路图像样本输入预先设置的语义分割模型。

[0052] 语义分割模型包括:空间金字塔编码器和解码器;空间金字塔编码器包括:多层卷积神经网络和空间金字塔模块。

[0053] 初始时,预先设置的语义分割模型中的网络参数为初值,通过样本训练之后,才可以进行图像分割。

[0054] 步骤106,通过多层卷积神经网络对SAR道路图像样本进行特征提取,将提取得到的浅层特征输入解码器的并联通道,将提取得到的深层特征输...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to an SAR image road extraction method and device based on semantic segmentation and a conditional random field. The method comprises the following steps: acquiring an SAR road image sample, inputting the SAR road image sample into a preset semantic segmentation model, carrying out feature extraction on the SAR road image sample through a multilayer convolutional neural network, inputting extracted shallow features into a parallel channel of a decoder, inputting the deep features obtained through extraction into a spatial pyramid module to be processed, obtaining the encoder features, inputting the encoder features into a decoder to be subjected to up-sampling and inputting the features into a parallel channel, and outputting a road extraction prediction result through multiple times of up-sampling; then introducing a second-order point pair full-connection conditional random field, and outputting an SAR image road. By adopting the method, the accuracy and comprehensiveness of road extraction can be improved.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a method and device for extracting roads from SAR images based on semantic segmentation and conditional random fields. Background technique [0002] Synthetic Aperture Radar (SAR) is an active imaging radar with multiple polarization modes and imaging conditions not affected by weather conditions. The single-polarization SAR extraction algorithm is based on the nature of specular scattering of radar waves by smooth roads, and mainly uses gray features to extract roads. The current method can be divided into threshold segmentation and region growing according to traditional image processing methods. With the deep learning in the field of computer vision With the rise of remote sensing data and the abundance of remote sensing data, semantic segmentation models have achieved significant performance improvements in SAR image pixel-level classification tasks such as sea an...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/34G06K9/46G06K9/62G06N3/04G06N3/08G06N7/00
CPCG06N3/08G06V20/182G06V10/267G06V10/40G06N7/01G06N3/045G06F18/253
Inventor 何奇山赵凌君赵琰张思乾唐涛熊博莅
Owner NAT UNIV OF DEFENSE TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Eureka Blog
Learn More
PatSnap group products