Nearest neighbor classification of remote sensing images with the maximal margin principle
Nearest neighbor classification of remote sensing images with the maximal margin principle
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DOI：
10.1109/tgrs.2008.916090
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发表时间：
2008-06-01
影响因子：
8.2
通讯作者：
Melgani, Farid
中科院分区：
文献类型：
--
作者：
Blanzieri, Enrico;Melgani, Farid
本文提出了一种新的基于最大间隔原则的k近邻(KNN)分类器。该方法依赖于通过首先找到其k-最近的训练样本来对给定的未标记样本进行分类。然后，通过在所考虑的k个训练样本上训练多类支持向量机分类器后确定的局部支持向量机(SVM)决策边界，对输入特征空间进行局部划分。未知样本的标记是通过查看它所属的局部决策区域来完成的。该方法的特点是得到了分段线性类型的全局决策边界。然而，整个过程可以通过使用基于所采用的核的简单重新公式的距离函数来确定变换后的特征空间中的k个最近的训练样本来实现。为了说明该方法的性能，对三个不同的遥感数据集进行了实验分析。
In this paper, we present a new variant of the k-nearest neighbor (kNN) classifier based on the maximal margin principle. The proposed method relies on classifying a given unlabeled sample by first finding its k-nearest training samples. A local partition of the input feature space is then carried out by means of local support vector machine (SVM) decision boundaries determined after training a multiclass SVM classifier on the considered k training samples. The labeling of the unknown sample is done by looking at the local decision region to which it belongs. The method is characterized by resulting global decision boundaries of the piecewise linear type. However, the entire process can be kernelized through the determination of the k-nearest training samples in the transformed feature space by using a distance function simply reformulated on the basis of the adopted kernel. To illustrate the performance of the proposed method, an experimental analysis on three different remote sensing datasets is reported and discussed.