SLIC super-pixel segmentation algorithm base on NMI features used in remote sensing image

Jing LI

doi:10.37188/OPE.20223006.0734

您当前的位置：

首页 >

文章列表页 >

SLIC super-pixel segmentation algorithm base on NMI features used in remote sensing image

Information Sciences | 更新时间：2022-04-26

- SLIC super-pixel segmentation algorithm base on NMI features used in remote sensing image
- Optics and Precision Engineering Vol. 30, Issue 6, Pages: 734-742(2022)
- 作者机构：
  
  北京跟踪与通信技术研究所，北京 100094
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20223006.0734
  CLC： TP391.4
- Received：21 September 2021，
  
  Revised：11 October 2021，
  
  Published：25 March 2022
- 稿件说明：
移动端阅览
李静.基于NMI特征的遥感影像线性迭代聚类超像素分割算法[J].光学精密工程,2022,30(06):734-742.

LI Jing.SLIC super-pixel segmentation algorithm base on NMI features used in remote sensing image[J].Optics and Precision Engineering,2022,30(06):734-742.
李静.基于NMI特征的遥感影像线性迭代聚类超像素分割算法[J].光学精密工程,2022,30(06):734-742. DOI： 10.37188/OPE.20223006.0734.

LI Jing.SLIC super-pixel segmentation algorithm base on NMI features used in remote sensing image[J].Optics and Precision Engineering,2022,30(06):734-742. DOI： 10.37188/OPE.20223006.0734.

摘要

针对现有基于简单线性迭代聚类（SLIC）的超像素分割算法用于细节丰富的遥感图像处理时，存在的易受噪声干扰、过分割问题，本文提出一种结合超像素块之间基于归一化转动惯量（NMI）特征的相似性度量的遥感影像分割方法，对分割效果进行改善。本文首先利用引导滤波算法对影像进行平滑处理，去除椒盐噪点；再通过现有的线性迭代聚类算法对影像进行像素级分割，生成初始的超像素；进而确定出微小超像素块，然后计算其与相邻超像素块的相似性度量值，将其合并入差异性最小的相邻超像素块，达到分割影像的目的。本文方法在传统分割算法基础上降低了超像素对噪声的敏感性，提高了影像分割的精度。实验表明，论文提出算法可将测试遥感图像的分割超像素块数量由4 171减小为282，微小超像素块数量减少60%以上，有效降低噪声点的影响，改善以往算法存在的过分割缺陷。

Abstract

The state-of-the-art super-pixel segmentation algorithm based on simple linear iterative clustering （SLIC） has the problem of over segmentation and discontinuity when processing remote sensing images with extensive details. Here， we propose a remote sensing image segmentation method that combines the NMI-based similarity measure between super-pixel blocks to improve the segmentation effect. First， a guided filtering is used to smooth the pepper noise in the image. Second， the image is segmented at a pixel level using the SLIC algorithm to generate initial super-pixels. Third， to achieve image segmentation， the micro super-pixels are determined based on some criterion and then merged into the adjacent super-pixel blocks with the least difference by calculating the similarity measure with its adjacent super-pixel blocks. This paper’s method reduces the sensitivity of super-pixel to noise and improves the precision of image segmentation compared with traditional segmentation algorithms. The experimental results indicate that the proposed algorithm reduced the number of segmented super-pixel blocks from 4 171 to 282 and reduced the number of micro super-pixel blocks by more than 60%. It also reduced the influence of noise points and improved the over segmentation defects of existing algorithms.

关键词

Keywords

references

窦鹏，翟亮，张继贤 . 基于GLC面向对象遥感影像分类方法的研究与应用［J］. 测绘与空间地理信息， 2013 ， 36 （ 11 ）： 68 - 71， 74 . doi: 10.3969/j.issn.1672-5867.2013.11.020 http://dx.doi.org/10.3969/j.issn.1672-5867.2013.11.020

DOU P ， ZHAI L ， ZHANG J X . Research and application of object- oriented remote sensing image classification based on GLC ［J］. Geomatics & Spatial Information Technology ， 2013 ， 36 （ 11 ）： 68 - 71， 74 . （in Chinese） . doi: 10.3969/j.issn.1672-5867.2013.11.020 http://dx.doi.org/10.3969/j.issn.1672-5867.2013.11.020

董心玉 . 基于面向对象的高分一号遥感影像森林分类研究［D］. 哈尔滨：东北林业大学， 2016 .

DONG X Y . An Object-based Forest Type Classification Research Based on GF- 1 Remote Sensing Data ［D］. Harbin ： Northeast Forestry University ， 2016 . （in Chinese）

REN ， MALIK . Learning a classification model for segmentation ［C］. Proceedings Ninth IEEE International Conference on Computer Vision. 1316，2003 ， Nice， France. IEEE ， 2003 ： 10 - 17 . doi: 10.1109/iccv.2003.1238308 http://dx.doi.org/10.1109/iccv.2003.1238308

STUTZ D ， HERMANS A ， LEIBE B . Superpixels： an evaluation of the state-of-the-art ［J］. Computer Vision and Image Understanding ， 2018 ， 166 ： 1 - 27 . doi: 10.1016/j.cviu.2017.03.007 http://dx.doi.org/10.1016/j.cviu.2017.03.007

ACHANTA R ， SHAJI A ， SMITH K ， et al . SLIC superpixels compared to state-of-the-art superpixel methods ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence ， 2012 ， 34 （ 11 ）： 2274 - 2282 . doi: 10.1109/tpami.2012.120 http://dx.doi.org/10.1109/tpami.2012.120

LIU Y J ， YU M J ， LI B J ， et al . Intrinsic manifold SLIC： a simple and efficient method for computing content-sensitive superpixels ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence ， 2018 ， 40 （ 3 ）： 653 - 666 . doi: 10.1109/tpami.2017.2686857 http://dx.doi.org/10.1109/tpami.2017.2686857

JAMPANI V ， SUN D ， LIU M Y ， et al . Superpixel sampling networks ［C］. Springer， The 4th Proceedings of the European Conference on Computer Vision . Munich ： Springer ， 2018 ： 363 - 380 . doi: 10.1007/978-3-030-01234-2_22 http://dx.doi.org/10.1007/978-3-030-01234-2_22

LEI T ， JIA X H ， ZHANG Y N ， et al . Superpixel-based fast fuzzy C-means clustering for color image segmentation ［J］. IEEE Transactions on Fuzzy Systems ， 2019 ， 27 （ 9 ）： 1753 - 1766 . doi: 10.1109/tfuzz.2018.2889018 http://dx.doi.org/10.1109/tfuzz.2018.2889018

ZHAO J X ， BO R ， HOU Q B ， et al . FLIC： Fast linear iterative clustering with active search ［J］. Computational Visual Media ， 2018 ， 4 （ 4 ）： 333 - 348 . doi: 10.1007/s41095-018-0123-y http://dx.doi.org/10.1007/s41095-018-0123-y

刘仲民，王阳，李战明，等 . 基于简单线性迭代聚类和快速最近邻区域合并的图像分割算法［J］. 吉林大学学报（工学版）， 2018 ， 48 （ 6 ）： 1931 - 1937 . doi: 10.13229/j.cnki.jdxbgxb20171009 http://dx.doi.org/10.13229/j.cnki.jdxbgxb20171009

LIU Z M ， WANG Y ， LI Z M ， et al . Image segmentation algorithm based on SLIC and fast nearest neighbor region merging ［J］. Journal of Jilin University （Engineering and Technology Edition）， 2018 ， 48 （ 6 ）： 1931 - 1937 . （in Chinese） . doi: 10.13229/j.cnki.jdxbgxb20171009 http://dx.doi.org/10.13229/j.cnki.jdxbgxb20171009

LU L Z ， WANG C ， YIN X . Incorporating texture into SLIC super-pixels method for high spatial resolution remote sensing image segmentation ［C］. 2019 8th International Conference on Agro-Geoinformatics （Agro-Geoinformatics）. 1619，2019 ， Istanbul， Turkey. IEEE ， 2019 ： 1 - 5 . doi: 10.1109/agro-geoinformatics.2019.8820692 http://dx.doi.org/10.1109/agro-geoinformatics.2019.8820692

HE K M ， SUN J ， TANG X O . Guided image filtering ［C］. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE ，： 1397- 1409 .

陆方杰，夏顺仁 . 基于归一化转动惯量的显微图像拼接算法［J］. 中国医疗器械杂志， 2007 ， 31 （ 6 ）： 404 - 406 . doi: 10.3969/j.issn.1671-7104.2007.06.004 http://dx.doi.org/10.3969/j.issn.1671-7104.2007.06.004

LU F J ， XIA S R . A microscopic image mosaicing algorithm based on normalized moment of inertia ［J］. Chinese Journal of Medical Instrumentation ， 2007 ， 31 （ 6 ）： 404 - 406 . （in Chinese） . doi: 10.3969/j.issn.1671-7104.2007.06.004 http://dx.doi.org/10.3969/j.issn.1671-7104.2007.06.004

NGUYEN T A ， SONG W S ， HONG M C . Spatially adaptive denoising algorithm for a single image corrupted by Gaussian noise ［J］. IEEE Transactions on Consumer Electronics ， 2010 ， 56 （ 3 ）： 1610 - 1615 . doi: 10.1109/tce.2010.5606304 http://dx.doi.org/10.1109/tce.2010.5606304

Views

700

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Vehicle detection method based on remote sensing image fusion of superpixel and multi-modal sensing network

Calibration of polarization axis direction of sub focal plane polarization image sensor

Recognition of human tumbles based on improved normalized inertia moment

Vehicle detection method based on remote sensing image fusion of superpixel and multi-modal sensing network

Related Author

LIAN Yuanfeng

LI Guangyang

SHEN Shaochen

Yang BAI

Kaichun ZHAO

Zheng YOU

LIU Li-wei

XUE Chun-fang

Related Institution

China University of Petroleum

Beijing Key Laboratory of Petroleum Data Mining， China University of Petroleum

Department of Precision Instrument， Tsinghua University

State Key Laboratory of Precision Measurement Technology and Instrument， Tsinghua University

College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰