Image and Signal Processing Methods

K-means algorithm based on stochastic distances for polarimetric synthetic aperture radar image classification

[+] Author Affiliations
Rogério Galante Negri, Tatiana Sussel Gonçalves Mendes

Universidade Estadual Paulista, Instituto de Ciência e Tecnologia, Departamento de Engenharia Ambiental, Rodovia Presidente Dutra, Km 137.8, Eugenio de Melo—12247-004, São José dos Campos, SP, Brazil

Wagner Barreto da Silva

Instituto Militar de Engenharia, Seção de Engenharia Cartográfica, Praça General Tibúrcio, 80, Praia Vermelha, Rio de Janeiro 22290-270, RJ, Brazil

J. Appl. Remote Sens. 10(4), 045005 (Oct 14, 2016). doi:10.1117/1.JRS.10.045005
History: Received June 10, 2016; Accepted September 21, 2016
Text Size: A A A

Abstract.  The availability of polarimetric synthetic aperture radar (PolSAR) images has increased, and consequently, the classification of such images has received immense attention. Among different classification methods in the literature, it is possible to distinguish them according to learning paradigm and approach. Unsupervised methods have as advantage the independence of labeled data for training. Regarding the approach, image classification can be performed based on its individual pixels or on previously identified regions in the image. Previous studies verified that the region-based classification of PolSAR images using stochastic distances can produce better results in comparison with the pixel-based. Faced with the independence of training data by unsupervised methods and the potential of the region-based approach with stochastic distances, this study proposes a version of the unsupervised K-means algorithm for PolSAR region-based classification based on stochastic distances. The Bhattacharyya stochastic distance between Wishart distributions was adopted to measure the dissimilarity among regions of the PolSAR image. Additionally, a measure was proposed to compare unsupervised classification results. Two case studies that consider real and simulated images were conducted, and the results showed that the proposed version of K-means achieves higher accuracy values in comparison with the classic version.

Figures in this Article
© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Rogério Galante Negri ; Wagner Barreto da Silva and Tatiana Sussel Gonçalves Mendes
"K-means algorithm based on stochastic distances for polarimetric synthetic aperture radar image classification", J. Appl. Remote Sens. 10(4), 045005 (Oct 14, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.045005


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.