PolSAR image segmentation based on the modified non-negative matrix factorization and support vector machine

Jianchao Fan, Jun Wang, Dongzhi Zhao

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

To improve polarimetric synthetic aperture radar (PolSAR) imagery segmentation accuracy, a modified non-negative matrix factorization algorithm based on the support vector machine is proposed. Focusing on PolSAR remote sensing images, the modified non-negative matrix factorization algorithm with the neurodynamic optimization achieves the image feature extraction. Compared with basic features, such as the basic backscatter coefficient, structuring more targeted localization non-negative character fits better for the physical significance of remote sensing images. Furthermore, based on the new constructive features, a support vector machine is employed for remote sensing image classification, which remedies the small sample training problem. Simulation results on PolSAR image classification substantiate the effectiveness of the proposed approach.

Original languageEnglish
Pages (from-to)594-601
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8866
DOIs
Publication statusPublished - 2014
Externally publishedYes

Keywords

  • Image segmentation
  • Non-negative matrix factorization
  • PolSAR
  • Support vector machine

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