Please wait a minute...
img

Wechat

Remote Sensing Technology and Application  2015, Vol. 30 Issue (3): 476-485    DOI: 10.11873/j.issn.1004-0323.2015.3.0476
    
A Two Stage Region Growing Method for Remote Sensing Image Segmentation
Su Tengfei,Li Hongyu
(Inner Mongolia Agricultural University,College of Water Conservancy and Civil Engineering,
Department of Surveying Engineering,Inner Mongolia Autonomous Region,Hohhot 010018,China)
Download:  PDF (5965KB) 
Export:  BibTeX | EndNote (RIS)      
Abstract  

How to improve the accuracy and speed of image segmentation algorithm that has a significant effect upon the interpretation of remote sensing images.In this paper,a region\|growing\|based method is proposed for remote sensing image segmentation.This method is composed of two procedures which are local the best merge and global best merge.The first step focuses on performing high\|speed image segmentation.In implementation,a threshold is introduced into the first step to reduce erroneous region merges.The second step aims at increasing segmentation accuracy.In order to raise the running speed,an advanced data structure and red\|black tree are utilized to implement the second step.Finally,simulated remote sensing image and Orbview\|3 high resolution images are used to carry out segmentation experiment.A supervised image segmentation accuracy evaluation method is utilized to quantitively validate the performance of our algorithm.The experiment result indicates that the proposed method can achieve satisfactory segmentation in terms of segmentation accuracy and speed.

Key words:  Remote sensing image segmentation      Region growing      Merging criterion     
Received:  18 March 2014      Published:  14 August 2015
TP 753  
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Su Tengfei
Li Hongyu

Cite this article: 

Su Tengfei,Li Hongyu. A Two Stage Region Growing Method for Remote Sensing Image Segmentation. Remote Sensing Technology and Application, 2015, 30(3): 476-485.

URL: 

http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2015.3.0476     OR     http://www.rsta.ac.cn/EN/Y2015/V30/I3/476

[1]Neubert M,Herold H,Meinel G.Evaluation of Remote Sensing Image Segmentation Quality-Further Results and Concepts[C]//Proceedings of the 1st International Conference on Object-based Image Analysis.Austria:Salzburg University,2006.

[2]Vieira M,Formaggio A,Rennó A,et al.Object based Image Analysis and Data Mining Applied to a Remotely Sensed Landsat Time-series to Map Sugarcane over Large Areas[J].Remote Sensing of Environment,2012,123(5):553-562.

[3]Carolyn E,Ronald J,Imants S,et al.Segmenting Multispectral Landsat TM Images into Field Units[J].IEEE Transactions on Geoscience and Remote Sensing,2002,40(5):1054-1064.

[4]Guo Xirong,Leng Xiaopeng,Wu Jianfeng.Study on Geological Disaster Information Management in Drainage Area based on High Resolution Remote Sensing Dat[J].Remote Sensing Technology and Apllication,2014,29(6):1081-1088.[郭曦榕,冷小鹏,吴剑峰.基于高分辨率遥感的流域级地质灾害信息管理研究[J].遥感技术与应用,2014,29(6):1081-1088.][5]Xu Suhui,Mu Xiaodong,Ke Bing,et al.A Study on Military Battle-field Surveillance Technique based on Remote Sensing Imagery[J].Remote Sensing Technology and Application,2014,29(3):511-516.[许夙晖,慕晓冬,柯冰,等.基于遥感影像的军事阵地动态监测技术研究[J].遥感技术与应用,2014,29(3):511-516.]

[6]Zhu C,Zhou H,Wang R,et al.A Novel Hierarchical Method of Ship Detection from Spaceborne Optical Image based on Shape and Texture Features[J].IEEE Transactions on Geoscience and Remote Sensing,2010,48(9):3446-3456.

[7]Ochilov S,Clausi D.Operational SAR Sea-ice Image Classification[J].IEEE Transactions on Geoscience and Remote Sensing,2012,50(11):4397-4408.

[8]Brekke C,Solberg A.Oil Spill Detection by Satellite Remote Sensing[J].Remote Sensing of Environment,2005,95(1):1-13.

[9]Yu H,Zhang X,Wang S,et al.Context-based Hierarchical Unequal Merging for SAR Image Segmentation[J].IEEE Transactions on Geoscience and Remote Sensing,2013,51(2):995-1009.

[10]Sheng G,Yang W,Deng X,et al.Coastline Detection in Synthetic Aperture Radar (SAR) Images by Integrating Watershed Transformation and Controllable Gradient Vector Flow (GVF) Snake Model[J].IEEE Oceanic Engineering,2012,37(3):375-383.

[11]Li N,Huo H,Zhao Y,et al.A Spatial Clustering Method with Edge Weighting for Image Segmentation[J].IEEE Geoscience and Remote Sensing Letters,2013,10(5):1124-1128.[12]Zhang Y,Rockettt I.The Bayesian Operating Point of the Canny Edge Detector[J].IEEE Transactions on Image Processing,2006,15(11):3409-3416.

[13]Beaulieu J,Goldberg M.Hierarchy in Picture Segmentation:A Stepwise Optimization Approach[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(2):150-163.

[14]Kurita T.An Efficient Agglomerative Clustering Algorithm for Region Growing[C]//IAPR Workshop on Machine Vision Application (MVA’94).Japan,Kawasaki,1994:210-213.

[15]Tilton J,Tarabalka Y,Montesno P,et al.Best Merge Region Growing with Integrated Non-adjacent Region Object Aggregation[J].IEEE Transactions on Geoscience and Remote Sensing,2012,50(11):4454-4467.

[16]Baatz M,Schpe A.Multiresolution Segmentation:An Optimizing Approach for High Quality Multi-Scale Segmentation[C]//Angewandte Geographich Informationsverarbeitung,XII.Germany,Wichmann,2000:12-23.

[17]Yu Q,Clausi D.IRGS:Image Segmentation Using Edge Penalties and Region Growing[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,30(12):2126-2139.

[18]Crevier D.Image Segmentation Algorithm Development Using Ground Truth Image Data Sets[J].Computer Vision and Image Understanding,2008,112(2):143-159.

No Suggested Reading articles found!