华中科技大学学报（自然科学版）Issue(3)：79-82,4.DOI:10.13245/j.hust.150316
基于加权投票集成的极化SAR图像分类方法
PolSAR image classification method based on weighted majority vote ensemble
摘要
Abstract
A polarimetric synthetic aperture radar (PolSAR ) image classification method based on weighted majority vote ensemble was proposed .The weighted majority vote ensemble was adopted to learn on different training samples ,in order to improve the classification results .Firstly ,the features were extracted from the PolSAR data ,and several groups of pixels in one class were chosen as the training sample subsets .After that ,the component classifiers learn on different training samples gave the predictive labels for the pixels ,and the weights were calculated on these labels .Finally ,the pre‐dictive labels were combined together to get the final classification result .The experimental results demonstrate the effectiveness of the proposed method on AIRSAR and Radarsat‐2 data .关键词
图像分类/雷达极化/监督分类/极化SAR图像分类/分类器集成/加权投票准则Key words
image classification/radar polarimetry/supervised classification/PolSAR image classifi-cation/classifer ensemble/weighted majority vote (WMV)分类
信息技术与安全科学引用本文复制引用
陈博,王爽,焦李成..基于加权投票集成的极化SAR图像分类方法[J].华中科技大学学报（自然科学版）,2015,(3):79-82,4.基金项目
国家重点基础研究发展计划资助项目（2013CB329402）；国家自然科学基金资助项目（61271302，61272282，61202176，61271298）；高等学校博士学科点专项科研基金资助项目（20100203120005）． （）
