High-precision segmentation extraction of harbor in remote sensing images based on F-B template

YANG Miao; SHEN Chen; GAO Zhi-sheng

doi:10.3788/OPE.20172513.0205

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High-precision segmentation extraction of harbor in remote sensing images based on F-B template

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- High-precision segmentation extraction of harbor in remote sensing images based on F-B template
- Optics and Precision Engineering Vol. 25, Issue 10s, Pages: 205-214(2017)
- 作者机构：
  
  西华大学计算机与软件工程学院, 四川成都 610039
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20172513.0205
  CLC： TP394.1;TH691.9
- Received：01 June 2017，
  
  Revised：19 June 2017，
  
  Published：25 November 2017
- 稿件说明：
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杨淼, 沈沉, 高志升. 基于F-B模板的遥感图像港口高精度分割提取[J]. 光学精密工程, 2017,25(10s): 205-214

YANG Miao, SHEN Chen, GAO Zhi-sheng. High-precision segmentation extraction of harbor in remote sensing images based on F-B template[J]. Editorial Office of Optics and Precision Engineering, 2017,25(10s): 205-214
杨淼, 沈沉, 高志升. 基于F-B模板的遥感图像港口高精度分割提取[J]. 光学精密工程, 2017,25(10s): 205-214 DOI： 10.3788/OPE.20172513.0205.

YANG Miao, SHEN Chen, GAO Zhi-sheng. High-precision segmentation extraction of harbor in remote sensing images based on F-B template[J]. Editorial Office of Optics and Precision Engineering, 2017,25(10s): 205-214 DOI： 10.3788/OPE.20172513.0205.

摘要

港口内舰船检测精度极度依赖于港口海陆分割的效果，港口内舰船停靠常常与岸堤相连，为降低港口分割检测难度，依据港口遥感图像特点，提出了一种港口高精度分割提取算法。首先通过遥感图像数据包含的经纬度坐标大致定位感兴趣的港口区域，然后依据港口对应的F（Feature）模板完成检测图像到B（Binary）模板的精确配准，最后根据B模板完成港口高精度分割提取。本文结果图像与模板图像大小、方向一致，具有精确的海陆分割，更有利于后续港口内舰船的高精度检测与识别。通过多组港口遥感图像进行实验，结果表明，与主流港口分割提取算法MLE、OTSU、Mean-shift、LBE和LBP相比，本文算法具有最高的港口分割检测精确度，无论是客观评价还是主管评价都优于同类方法，RUMA评价指标相比同类方法取得显著提高，多组实验相比当前最好方法平均提高2.5倍。

Abstract

Detection precision of ship in harbor was extremely dependent on the effect of sea-land segmentation in the harbor. Docking of ships in the harbor was usually connected with embankment

which increased the difficulty of harbor segmentation and detection. A high-precision segmentation extraction algorithm of harbor was proposed according to remote sensing image features of the harbor. Interested harbor areas were roughly located by latitude and longitude coordinates contained in remote sensing image data

and then accurate registration from detection image to B (Binary) template was finished according to corresponding F (Feature) of harbor; finally

high-precision segmentation and extraction of harbor was finished according to B template. Resulting images in the thesis were consistent with size and direction of template images

had accurate sea-land segmentation and were more beneficial to high-precision detection and recognition of ships in subsequent harbors. Through experiment by multiple groups of remote sensing images of the harbor

the result shows that algorithm in the thesis has the highest segmentation detection precision of harbor compared with segmentation extraction algorithm of mainstream harbor such as MLE

OTSU

Mean-shift

LBE and LBP and it is superior to other similar methods in both objective evaluation and subjective evaluation. RUMA evaluation index is improved significantly compared with other similar methods

and the best method at present has increased by 2.5 times at average compared with multiple groups of experiments.

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references

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