局部自适应方向模板匹配的高分辨率遥感影像道路提取

孙可; 张钧萍

doi:10.3788/OPE.20152313.0509

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局部自适应方向模板匹配的高分辨率遥感影像道路提取

更新时间：2020-08-13

- 局部自适应方向模板匹配的高分辨率遥感影像道路提取
- Road extraction from high-resolution remote sensing imagery based on local adaptive directional template match
- 光学精密工程 2015年23卷第10z期页码：509-515
- 作者机构：
  
  哈尔滨工业大学电子与信息工程学院,黑龙江哈尔滨,150001
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.61271348)()
- DOI：10.3788/OPE.20152313.0509
  中图分类号： TP751
- 收稿日期：2015-03-25，
  
  修回日期：2015-04-03，
  
  纸质出版日期：2015-11-14
- 稿件说明：
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孙可, 张钧萍,. 局部自适应方向模板匹配的高分辨率遥感影像道路提取[J]. 光学精密工程, 2015,23(10z): 509-515

SUN Ke, ZHANG Jun-ping,. Road extraction from high-resolution remote sensing imagery based on local adaptive directional template match[J]. Editorial Office of Optics and Precision Engineering, 2015,23(10z): 509-515
孙可, 张钧萍,. 局部自适应方向模板匹配的高分辨率遥感影像道路提取[J]. 光学精密工程, 2015,23(10z): 509-515 DOI： 10.3788/OPE.20152313.0509.

SUN Ke, ZHANG Jun-ping,. Road extraction from high-resolution remote sensing imagery based on local adaptive directional template match[J]. Editorial Office of Optics and Precision Engineering, 2015,23(10z): 509-515 DOI： 10.3788/OPE.20152313.0509.

摘要

提出了一种基于局部自适应方向模板匹配的高分辨率遥感影像道路提取方法。该方法利用局部自适应方向模板匹配对遥感图像进行初步分割

得到二值化的粗糙道路段;利用局部灰度一致性原理对输入图像进行一致性检测

得到图像中各像素点的局部灰度检测结果。将初步分割结果和局部灰度一致性检测结果进行信息融合

获得较纯净的初始道路段。最后

使用道路的形状特征对经信息融合后的结果进行滤波

得到初始道路提取结果。由于道路在空间上是连续的

所以在图像中道路的面积不会太小

利用面积指数剔除小面积区域

利用长宽比指数剔除非道路信息

即可获得提取结果。采用2幅高分辨率遥感影像对算法进行了验证

并与现有的道路提取方法进行了对比。实验结果表明

该方法能较好地提取出道路信息。

Abstract

An approach of road information extraction for the high-resolution remote sensing imagery was proposed based on local adaptive directional template match. The remote sensing imagery was segmented by means of the local adaptive directional template match and a binary image of the coarse road was obtained. Guided by the principle of local gray-scale consistency

the consistency of the input images was tested and the test results on local gray-scale of all the pixels in the image were received. Afterwards

the preliminary segmented results and the results of local gray-scale consistency test were fused to get a better original part of the road. Finally

the results after information fusion were filtered according to the different features of the road shapes and the original part of the road was attained. The areas of the road in the image are not too small as the road is successive from the space aspect. Accordingly

the parts of smaller area were removed on the basis of area index and non-road information was also removed based on the length-width ratio

so that the exacted results were obtained. The algorithm was verified by two high-resolution remote sensing images and it was compared with known road information exaction methods were accomplished. The results show that the road information can be more successfully exacted by this approach as shown in the experiment results.

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Keywords

references

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