Class-Specific Feature Selection With Local Geometric Structure and Discriminative Information Based on Sparse Similar Samples

Chen Xi; Gu Yanfeng

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Class-Specific Feature Selection With Local Geometric Structure and Discriminative Information Based on Sparse Similar Samples

机译：基于稀疏相似样本的具有局部几何结构和判别信息的特定类别特征选择

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摘要

It is necessary while quite challenging to select features strongly relevant to a thematic class, i.e., class-specific features, from very high resolution (VHR) remote sensing images. To meet this challenge, a class-specific feature selection method based on sparse similar samples (CFS4) is proposed. Specifically, CFS4 incorporates the local geometrical structure and discriminative information of the data into a sparsity regularization problem. The experimental results on VHR satellite images well validate the effectiveness and practicability of the proposed method.

机译：从非常高分辨率（VHR）的遥感图像中选择与主题课程密切相关的特征，即特定于类的特征，这是非常困难的。为了应对这一挑战，提出了一种基于稀疏相似样本的特定类特征选择方法（CFS4）。具体而言，CFS4将数据的局部几何结构和判别信息合并到稀疏正则化问题中。在VHR卫星图像上的实验结果很好地证明了该方法的有效性和实用性。

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《Geoscience and Remote Sensing Letters, IEEE》 |2015年第7期|1392-1396|共5页
作者
Chen Xi; Gu Yanfeng;
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正文语种 eng
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Accuracy; Computational modeling; Feature extraction; Information retrieval; Object oriented modeling; Remote sensing; Support vector machines; Class-based features; object-oriented image analysis; remote sensing; supervised feature selection;

机译：精度;计算建模;特征提取;信息检索;面向对象建模;遥感;支持向量机;基于类的特征;面向对象图像分析;遥感;监督特征选择;

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Class-Specific Feature Selection With Local Geometric Structure and Discriminative Information Based on Sparse Similar Samples

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