For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
A Binary Tree Structured Terrain Classifier for Pol-SAR Images
Guangyi ZHOU Yi CUI Yumeng LIU Jian YANG
IEICE TRANSACTIONS on Communications
Publication Date: 2011/05/01
Online ISSN: 1745-1345
Print ISSN: 0916-8516
Type of Manuscript: LETTER
Synthetic Aperture Radar (SAR), polarimetry, classification, texture, binary tree, Support Vector Machine (SVM),
Full Text: PDF(370.8KB)>>
In this letter, a new terrain type classifier is proposed for polarimetric Synthetic Aperture Radar (Pol-SAR) images. This classifier uses the binary tree structure. The homogenous and inhomogeneous areas are first classified by the support vector machine (SVM) classifier based on the texture features extracted from the span image. Then the homogenous and inhomogeneous areas are, respectively, classified by the traditional Wishart classifier and the SVM classifier based on the texture features. Using a NASA/JPL AIRSAR image, the authors achieve the classification accuracy of up to 98%, demonstrating the effectiveness of the proposed method.