Supervised feature-based classification of multi-channel SAR images
Supervised feature-based classification of multi-channel SAR images
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DOI：
10.1016/j.patrec.2005.08.006
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发表时间：
2006-03
期刊：
影响因子：
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通讯作者：
D. Borghys;Y. Yvinec;C. Perneel;A. Pižurica;W. Philips
中科院分区：
文献类型：
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作者：
D. Borghys;Y. Yvinec;C. Perneel;A. Pižurica;W. Philips
本文描述了一种基于特征的多通道SAR数据监督分类的新方法。由于输入特征的统计分布多样，经典的特征选择和分类方法是不够的。因此，提出了一种基于逻辑回归（LR）和多项逻辑回归（MNLR）的用于分离不同类别的方法。 LR 和 MNLR 这两种方法都较少依赖于输入数据的统计分布。还引入了一种新的空间正则化方法来增加分类结果的一致性。该分类方法应用于人道主义排雷项目，其中相关类别由地雷行动中心的专家定义。地面调查任务收集了每个班级的学习和验证样本。显示了所提出的分类方法的结果并与最大似然分类器进行比较。
This paper describes a new method for a feature-based supervised classification of multi-channel SAR data. Classic feature selection and classification methods are inadequate due to the diverse statistical distributions of the input features. A method based on logistic regression (LR) and multinomial logistic regression (MNLR) for separating different classes is therefore proposed. Both methods, LR and MNLR, are less dependent on the statistical distribution of the input data. A new spatial regularization method is also introduced to increase consistency of the classification result. The classification method was applied to a project on humanitarian demining in which the relevant classes were defined by experts of a mine action center. A ground survey mission collected learning and validation samples for each class. Results of the proposed classification methods are shown and compared to a maximum likelihood classifier.