Adapting Remote Sensing to New Domain With ELM Parameter Transfer
Adapting Remote Sensing to New Domain With ELM Parameter Transfer
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DOI：
10.1109/lgrs.2017.2726760
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发表时间：
2017-09
影响因子：
4.8
通讯作者：
Suhui Xu;Xiaodong Mu;Dong Chai;Shuyang Wang
中科院分区：
文献类型：
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作者：
Suhui Xu;Xiaodong Mu;Dong Chai;Shuyang Wang
对未标记的遥感图像进行注释非常耗时。一种策略是将来自另一个领域的标记遥感图像作为训练样本，通过监督分类来预测目标遥感标签。然而，由于两个域之间的分布差异，这可能会导致负迁移。为了解决这个问题，我们通过传输极限学习机（ELM）的参数提出了一种新的领域适应方法。该方法的核心是学习一个变换，将目标域的ELM参数映射到源域，使得目标域的分类器参数与源域最大程度地对齐。我们的方法具有以前在单一方法中无法实现的几个优点：通过参数传递进行多类适应，同时学习最终的分类器和转换，以及避免负迁移。我们对三个数据集进行了实验，结果表明与基线方法相比，准确性和计算优势有所提高。
It is time consuming to annotate unlabeled remote sensing images. One strategy is taking the labeled remote sensing images from another domain as training samples, and the target remote sensing labels are predicted by supervised classification. However, this may lead to negative transfer due to the distribution difference between the two domains. To address this issue, we propose a novel domain adaptation method through transferring the parameters of extreme learning machine (ELM). The core of this method is learning a transformation to map the target ELM parameters to the source, making the classifier parameters of the target domain maximally aligned with the source. Our method has several advantages which was previously unavailable within a single method: multiclass adaptation through parameter transferring, learning the final classifier and transformation simultaneously, and avoiding negative transfer. We perform experiments on three data sets that indicate improved accuracy and computational advantages compared to baseline approaches.