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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Semisupervised Hyperspectral Image Classification With Cluster-Based Conditional Generative Adversarial Net
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Semisupervised Hyperspectral Image Classification With Cluster-Based Conditional Generative Adversarial Net

机译：基于基于簇的条件生成对抗网的半精化高光谱图像分类

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摘要

Hyperspectral image classification is a challenging task when a limited number of training samples are available. It is also known that the classification performance highly depends on the quality of the labeled samples. In this work, a cluster-based conditional generative adversarial net (CCGAN) is proposed as an effective solution to increase the size and quality of the training data set. The proposed method is able to automatically select the most representative initial samples with a subtractive clustering-based strategy, which keeps the diversity for sample generation. Moreover, compared to the traditional semisupervised classification frameworks, the CCGAN is able to generate realistic spectral profiles by considering the class-specific labels. Experiments on well-known Pavia University data set demonstrate that the proposed CCGAN can significantly boost the classification accuracy, even using a small number of initial labeled samples.
机译：当有限数量的训练样本可用时，高光谱图像分类是一个具有挑战性的任务。还已知分类性能高度取决于标记样本的质量。在这项工作中，提出了一种基于群集的条件生成对冲网（CCGAN）作为增加培训数据集的大小和质量的有效解决方案。所提出的方法能够通过基于减法的聚类的策略自动选择最代表性的初始样本，这使得样品生成的多样性。此外，与传统的半熟种分类框架相比，CCGAN能够通过考虑特定于类的标签来产生现实的光谱配置文件。众所周知的帕维亚大学数据集的实验表明，即使使用少量初始标记的样本，也可以显着提高分类准确性。

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  • 来源
    《IEEE Geoscience and Remote Sensing Letters》 |2020年第3期|539-543|共5页
  • 作者

    Zhao Wenzhi; Chen Xuehong; Bo Yanchen; Chen Jiage;

  • 作者单位

    Beijing Normal Univ Fac Geog Sci Inst Remote Sensing Sci & Engn State Key Lab Remote Sensing Sci Beijing 100875 Peoples R China|Beijing Normal Univ Fac Geog Sci Beijing Engn Res Ctr Global Land Remote Sensing P Inst Remote Sensing Sci & Engn Beijing 100875 Peoples R China;

    Beijing Normal Univ Fac Geog Sci Inst Remote Sensing Sci & Engn State Key Lab Remote Sensing Sci Beijing 100875 Peoples R China|Beijing Normal Univ Fac Geog Sci Beijing Engn Res Ctr Global Land Remote Sensing P Inst Remote Sensing Sci & Engn Beijing 100875 Peoples R China;

    Beijing Normal Univ Fac Geog Sci Inst Remote Sensing Sci & Engn State Key Lab Remote Sensing Sci Beijing 100875 Peoples R China|Beijing Normal Univ Fac Geog Sci Beijing Engn Res Ctr Global Land Remote Sensing P Inst Remote Sensing Sci & Engn Beijing 100875 Peoples R China;

    Beijing Normal Univ Fac Geog Sci Inst Remote Sensing Sci & Engn State Key Lab Remote Sensing Sci Beijing 100875 Peoples R China|Beijing Normal Univ Fac Geog Sci Beijing Engn Res Ctr Global Land Remote Sensing P Inst Remote Sensing Sci & Engn Beijing 100875 Peoples R China;

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  • 正文语种 eng
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  • 关键词

    Generative adversarial nets (GANs); hyperspectral images; image classification; semisupervised learning (SSL);

    机译：生成的对抗网（GANS）;高光谱图像;图像分类;半草学习（SSL）;

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