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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Classifier-Constrained Deep Adversarial Domain Adaptation for Cross-Domain Semisupervised Classification in Remote Sensing Images
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Classifier-Constrained Deep Adversarial Domain Adaptation for Cross-Domain Semisupervised Classification in Remote Sensing Images

机译：分类器 - 在遥感图像中为跨域半化分类进行约束的深度逆境域适应

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摘要

This letter presents a classifier-constrained deep adversarial domain adaptation (CDADA) method for cross-domain semisupervised classification in remote sensing (RS) images. A deep convolutional neural network (DCNN) is used to build feature representations to describe the semantic content of scenes before the adaptation process. Then, adversarial domain adaptation is used to align the feature distribution of the source and the target. Specifically, two different land-cover classifiers are used as a discriminator to consider land-cover decision boundaries between classes and increase their distance to separate them from the original land-cover class boundaries. The generator then creates robust transferable features far from the original land-cover class boundaries under the classifier constraint. The experimental results of six scenarios built from three benchmark RS scene data sets (AID, Merced, and RSI-CB data sets) are reported and discussed.
机译：这封信介绍了遥感（RS）图像中的跨域半培训分类的分类器受限的深度对抗域适应（CDADA）方法。深度卷积神经网络（DCNN）用于构建特征表示，以描述自适应过程之前的场景的语义内容。然后，对抗对抗域的适配来对准源和目标的特征分布。具体地，两个不同的陆地覆盖分类器用作鉴别器，以考虑类别之间的陆地判定界限，并增加其距离，以将它们与原始陆地覆盖类边界分开。然后，发电机在分类器约束下创建远离原始陆地覆盖类边界的鲁棒可转移特征。报告并讨论了由三个基准RS场景数据集（AID，Merced和RSI-CB数据集）构建的六种情况的实验结果。

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