Comparing matrix distance measures for unsupervised POLSAR data classification of sea ice based on agglomerative clustering
Abstract:Clustering is a technique that can be applied for unsupervised classification of polarimetric synthetic aperture radar (POLSAR) data, an important analysis technique of radar polarimetry. Six matrix distance measures have been investigated and compared through an agglomerative clustering of RADARSAT-2 POLSAR data. The considered matrix distance measures were used as similarity criteria for merging clusters hierarchically into an appropriate number of classes. In this study, the considered distances are Manhattan, Euclidean, Bartlett, revised Wishart, Wishart test statistic, and Wishart Chernoff. Results show that the Bartlett, revised Wishart, and Wishart Chernoff distances all produce identical classification results. The Manhattan distance retrieved classification results close to those obtained by the Euclidean distance. The Bartlett, revised Wishart, and Wishart Chernoff distances produced the most accurate classification results. The study area is located in Hudson Bay offshore Churchill, Manitoba, Canada.
Document Type: Research Article
Affiliations: 1: Department of Geomatics Engineering, Schulich School of Engineering,University of Calgary, Calgary,Alberta, CanadaT2N 1N4, 2: Department of Geography,University of Calgary, Calgary,Alberta, CanadaT2N 1N4, 3: Department of Geosciences, School of Natural Sciences and Mathematics,The University of Texas at Dallas, Richardson,TX,75080, USA
Publication date: February 20, 2013