AdaBoost在偏振SAR图像分类中的应用

2009 IEEE Radar Conference Pub Date : 2009-05-04 DOI:10.1109/RADAR.2009.4976988

Rui Min, Xiaobo Yang, Zhiqin Zhao

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引用次数: 6

摘要

提出了一种基于极化分解和AdaBoost算法的极化SAR图像分类方法。该方法提高了分类精度和分类速度。AdaBoost算法作为一种鲁棒学习算法，可以充分利用极化特征实现图像分类，具有较高的学习精度。在仿真实验中，与H /α′s分类算法相比，该方法具有更高的分类精度和分类速度。

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Application of AdaBoost in polarimetric SAR image classification

In this paper, a method of polarimetric SAR image classification based on polarimetric decomposition and AdaBoost algorithm is proposed. The proposed method improves classification accuracy and speed. AdaBoost algorithm, as a robust learner with high accuracy, can fully utilize the polarimetric features to achieve image classification. In simulated tests, the proposed method is observed to produce improved classification accuracy and speed, compared with H /α̅ classification algorithm.

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2009 IEEE Radar Conference

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