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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >One-Class Classification of Remote Sensing Images Using Kernel Sparse Representation
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One-Class Classification of Remote Sensing Images Using Kernel Sparse Representation

机译：基于核稀疏表示的一类遥感影像分类

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摘要

Sparse representations have been widely studied in remote sensing image analysis in recent years. In this paper, we develop a novel method for one-class classification (OCC) using a kernel sparse representation model for remotely sensed imagery. Training samples taken from the target class alone are used to build a learning dictionary for the sparse representation model, which is then optimized to produce a reconstruction residual. In the proposed model, a pixel is classified as the target class if the obtained reconstruction residual for the pixel is smaller than a given threshold; otherwise, the pixel is labeled as the outlier class. To improve the data separability between the target and outliner classes, the training samples taken from the target class are mapped into a high-dimensional feature space using a kernel function to build a learning dictionary for the kernel sparse representation model. OCC is then conducted in the mapped high-dimensional feature space using the reconstruction residual threshold, following the same principle as OCC in the original feature space. The proposed OCC method is evaluated and compared with several existing OCC methods in three different case studies. The experimental results indicate that the proposed method outperforms these existing methods, particularly when using a kernel sparse representation.
机译：近年来，稀疏表示已在遥感图像分析中得到广泛研究。在本文中，我们使用一种用于遥感影像的内核稀疏表示模型开发了一种用于一类分类（OCC）的新方法。仅从目标类别中获取的训练样本用于构建稀疏表示模型的学习词典，然后对其进行优化以生成重构残差。在提出的模型中，如果获得的像素重建残差小于给定阈值，则将像素分类为目标类别。否则，将像素标记为离群值类别。为了提高目标类和大纲类之间的数据可分离性，使用内核函数将来自目标类的训练样本映射到高维特征空间中，从而为内核稀疏表示模型构建学习词典。然后，按照与原始特征空间中OCC相同的原理，使用重构残差阈值在映射的高维特征空间中进行OCC。在三种不同的案例研究中，对提出的OCC方法进行了评估，并与几种现有的OCC方法进行了比较。实验结果表明，所提出的方法优于现有方法，特别是在使用内核稀疏表示时。

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