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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Triplet Adversarial Domain Adaptation for Pixel-Level Classification of VHR Remote Sensing Images
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Triplet Adversarial Domain Adaptation for Pixel-Level Classification of VHR Remote Sensing Images

机译：三联普通域域适应VHR遥感图像的像素级分类

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摘要

Pixel-level classification for very high resolution (VHR) images is a crucial but challenging task in remote sensing. However, since the diverse ways of satellite image acquisition and the distinct structures of various regions, the distributions of the same semantic classes among different data sets are dissimilar. Therefore, the classification model trained on one data set (source domain) may collapse, when it is directly applied to another one (target domain). To solve this problem, many adversarial-based domain adaptation methods have been proposed. However, these methods only consider the source and the target domains independently in the adversarial training, where only the target domain is explicitly contributed to narrow the gap between the distributions of both domains. Unlike previous methods, we propose a triplet adversarial domain adaptation (TriADA) method that jointly considers both domains to learn a domain-invariant classifier by a novel domain similarity discriminator. Specifically, the discriminator takes a triplet of segmentation maps as input, where two segmentation maps from the same domain are to be distinguished from the two maps from the different domains during the adversarial learning. Consequently, it explicitly considers both domains' information to narrow the distribution gap across domains. To enhance the discriminability of the classifier on the target domain, a class-aware self-training strategy, which depends on the output of the discriminator, is proposed to assign pseudo-labels with high adapted confidence on target data to retrain the classifier. Extensive experiments on several VHR pixel-level classification benchmarks demonstrate the effectiveness of our method as well as its superiority to the-state of the art.
机译：非常高分辨率（VHR）图像的像素级分类是遥感中的重要而具有挑战性的任务。然而，由于卫星图像采集的不同方式和各个区域的不同结构，不同数据集之间的相同语义类的分布是不同的。因此，当它直接应用于另一个（目标域）时，在一个数据集（源域）上训练的分类模型可能会崩溃。为了解决这个问题，已经提出了许多基于对抗的域适应方法。然而，这些方法仅在对冲训练中独立地考虑源和目标域，其中仅明确地促进目标域以缩小两个域的分布之间的间隙。与以前的方法不同，我们提出了一种三重势地域适应（三合一）方法，该方法共同考虑域通过新颖的域相似性鉴别器学习域不变分类器。具体地，鉴别器将分割映射的三重态作为输入，其中来自同一域的两个分段图将与来自不同域期间的两种地图区分开。因此，它明确地考虑了域名的信息，以缩小域跨域的分布差距。为了增强分类器对目标域上的分类性，提出了一种依赖于鉴别器的输出的类感知自我训练策略，以指定具有高适应目标数据的伪标签来重新分类。关于几个VHR像素级分类基准的广泛实验证明了我们方法的有效性以及其对最先进的优势。

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  • 作者

    Yan Liang; Fan Bin; Liu Hongmin; Huo Chunlei; Xiang Shiming; Pan Chunhong;

  • 作者单位

    Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China|Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China;

    Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China|Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China;

    Henan Polytech Univ Sch Comp Sci & Technol Jiaozuo 454000 Henan Peoples R China|Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China;

    Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China;

    Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China|Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China;

    Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Domain adaptation (DA); pixel-level classification; self-training; triplet adversarial learning; very high resolution (VHR);

    机译：域适应（DA）;像素级分类;自我训练;三重态越野魄的学习;非常高分辨率（VHR）;

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