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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Robust Hyperspectral Image Domain Adaptation With Noisy Labels
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Robust Hyperspectral Image Domain Adaptation With Noisy Labels

机译：带有高噪声标签的鲁棒高光谱图像域自适应

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摘要

In hyperspectral image (HSI) classification, domain adaptation (DA) methods have been proved effective to address unsatisfactory classification results caused by the distribution difference between training (i.e., source domain) and testing (i.e., target domain) pixels. However, these methods rely on accurate labels in source domain, and seldom consider the performance drop resulted by noisy label, which often happens since labeling pixel in HSI is a challenging task. To improve the robustness of DA method to label noise, we propose a new unsupervised HSI DA method, which is constructed from both feature-level and classifier-level. First, a linear transformation function is learned in feature-level to align the source (domain) subspace with the target (domain) subspace. Then, a robust low-rank representation based classifier is developed to well cope with the features obtained from the aligned subspace. Since both subspace alignment and the classifier are immune to noisy labels, the proposed method obtains good classification results when confronting with noisy labels in source domain. Experimental results on two DA benchmarks demonstrate the effectiveness of the proposed method.
机译：在高光谱图像（HSI）分类中，域适应（DA）方法已被证明有效地解决了由于训练（即源域）和测试（即目标域）像素之间的分布差异而导致的令人满意的分类结果。但是，这些方法依赖于源域中的准确标签，很少考虑噪声标签导致的性能下降，这种情况经常发生，因为在HSI中标记像素是一项艰巨的任务。为了提高DA方法标记噪声的鲁棒性，我们提出了一种新的无监督HSI DA方法，该方法是从特征级和分类器级构造的。首先，在特征级别学习线性变换函数，以将源（域）子空间与目标（域）子空间对齐。然后，开发了一种基于低秩表示的鲁棒分类器，以很好地应对从对齐子空间获得的特征。由于子空间对齐和分类器都不受噪声标签的影响，因此在源域中遇到噪声标签时，该方法获得了很好的分类结果。在两个DA基准上的实验结果证明了该方法的有效性。

著录项

  • 来源
    《IEEE Geoscience and Remote Sensing Letters》 |2019年第7期|1135-1139|共5页
  • 作者

    Wei Wei; Li Wei; Zhang Lei; Wang Cong; Zhang Peng; Zhang Yanning;

  • 作者单位

    Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Shaanxi, Peoples R China|Northwestern Polytech Univ, Natl Engn Lab Integrated Aerospace Ground Ocean B, Xian 710072, Shaanxi, Peoples R China|Northwestern Polytech Univ, Shaanxi Prov Key Lab Speech & Image Informat Proc, Xian 710072, Shaanxi, Peoples R China;

    Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Shaanxi, Peoples R China|Northwestern Polytech Univ, Shaanxi Prov Key Lab Speech & Image Informat Proc, Xian 710072, Shaanxi, Peoples R China;

    Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Shaanxi, Peoples R China|Northwestern Polytech Univ, Shaanxi Prov Key Lab Speech & Image Informat Proc, Xian 710072, Shaanxi, Peoples R China;

    Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Shaanxi, Peoples R China|Northwestern Polytech Univ, Shaanxi Prov Key Lab Speech & Image Informat Proc, Xian 710072, Shaanxi, Peoples R China;

    Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Shaanxi, Peoples R China|Northwestern Polytech Univ, Shaanxi Prov Key Lab Speech & Image Informat Proc, Xian 710072, Shaanxi, Peoples R China;

    Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Shaanxi, Peoples R China|Northwestern Polytech Univ, Natl Engn Lab Integrated Aerospace Ground Ocean B, Xian 710072, Shaanxi, Peoples R China|Northwestern Polytech Univ, Shaanxi Prov Key Lab Speech & Image Informat Proc, Xian 710072, Shaanxi, Peoples R China;

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  • 正文语种 eng
  • 中图分类
  • 关键词

    Domain adaptation (DA); hyperspectral image (HSI) classification; low-rank representation; subspace alignment;

    机译：域自适应（DA）;高光谱图像（HSI）分类;低秩表示;子空间对齐;

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