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首页> 外文会议>International Conference on Geoinformatics >An Unsupervised Domain Adaptation Method for Multi-Modal Remote Sensing Image Classification
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An Unsupervised Domain Adaptation Method for Multi-Modal Remote Sensing Image Classification

机译：多模态遥感图像分类的无监督域自适应方法

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摘要

Labeling remote sensing data for classification is costly and time-consuming in practical applications, while sufficient and representative labels are critical for achieving a high accuracy. Transfer learning emerges as an effective method for this issue by reusing samples from other domains. In this paper, we propose an unsupervised domain adaptation method which can align the marginal distribution and conditional distribution in source and target domain at the same time. Our method treats the importance of the marginal and conditional distribution discrepancies at different levels and maps the feature sets of source domain and target domain into Reproducing Kernel Hilbert Space (RKHS) to obtain similar feature sets. In particular, we apply the proposed method on the multi-modal remote sensing data including pixel-wise overlaid Orthophoto and Digital Surface Models (DSM). With experiments containing images of different cities with highly distinguishable land-cover patterns as source and target domain, we demonstrate that, as compared to several state-of-the-art domain adaptation (DA) algorithms, our method can achieve a satisfactory performance on the target domain by a simple statistical classifier trained only by samples in the source domain.
机译：标记用于分类的遥感数据在实际应用中昂贵且耗时，而足够和代表性的标签对于实现高精度至关重要。转移学习通过重用来自其他领域的样本来出现为此问题的有效方法。在本文中，我们提出了一种无监督的域适应方法，可以同时对准源和靶域中的边缘分布和条件分布。我们的方法对不同级别的边缘和条件分布差异的重要性，并将源域和目标域的特征组映射到再现内核HILBERT空间（RKHS）以获得类似的特征集。特别是，我们在包括像素 - 明智的覆盖器和数字表面模型（DSM）的多模态遥感数据上应用所提出的方法。通过含有不同城市的图像的实验，具有高度可区别的陆地覆盖模式作为源域，我们证明，与若干最先进的域适应（DA）算法相比，我们的方法可以实现令人满意的性能目标域仅由源域中的样本训练的简单统计分类器。

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