A TSVM based semi-supervised approach to SAR Image Segmentation

Ji Jun*, Shao Fengjing, Sun Rencheng, Zhang Neng, Liu Guanfeng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

6 Citations (Scopus)

Abstract

Image segmentation is a fundamental issue in image processing. Segmentation of synthetic aperture radar (SAR) images is extremely difficult on account of intrinsic multiplicative speckle noises. Due to the ambiguities of SAR images, labeled instances are difficult and time-consuming to obtain while unlabeled data are abundant. In this paper, a new semi-supervised approach based on transductive support vector machine (TSVM) is proposed to segment SAR images, it is robust to noises and is effective when dealing with low numbers of high-dimensional samples, moreover, it could efficiently make use of unlabeled data to reduce human labor and improve precision. Segmentation results are also compared to SVM and TSVM trained by using different samples and parameters. Experimental results demonstrate that the proposed method is very promising.

Original languageEnglish
Title of host publication2008 International Workshop on Education Technology and Training and 2008 International Workshop on Geoscience and Remote Sensing, ETT and GRS 2008
Pages495-498
Number of pages4
Volume1
DOIs
Publication statusPublished - 2009
Event2008 International Workshop on Education Technology and Training and 2008 International Workshop on Geoscience and Remote Sensing, ETT and GRS 2008 - Shanghai, China
Duration: 21 Dec 200822 Dec 2008

Other

Other2008 International Workshop on Education Technology and Training and 2008 International Workshop on Geoscience and Remote Sensing, ETT and GRS 2008
CountryChina
CityShanghai
Period21/12/0822/12/08

Keywords

  • Image segmentation
  • SAR
  • Semisupervised
  • TSVM

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  • Cite this

    Jun, J., Fengjing, S., Rencheng, S., Neng, Z., & Guanfeng, L. (2009). A TSVM based semi-supervised approach to SAR Image Segmentation. In 2008 International Workshop on Education Technology and Training and 2008 International Workshop on Geoscience and Remote Sensing, ETT and GRS 2008 (Vol. 1, pp. 495-498). [5070204] https://doi.org/10.1109/ETTandGRS.2008.13