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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Convolutional Neural Network-Based Transfer Learning for Optical Aerial Images Change Detection
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Convolutional Neural Network-Based Transfer Learning for Optical Aerial Images Change Detection

机译：基于卷积神经网络的光学天线图像变化检测的转移学习

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摘要

Considering the lack of labeled training data sets for the supervised change detection task, in this letter, we try to relieve this problem by proposing a convolutional neural network (CNN)-based change detection method with a newly designed loss function to achieve transfer learning among different data sets. To reach this goal, we first pretrain a U-Net model on an open source data set by taking advantages of the relatively sufficient training data used for the supervised semantic segmentation task. Then, we minimize a skillfully designed loss function to combine the high-level features extracted from the pretrained model and the semantic information contained in the change detection data set, by which a transfer learning is achieved. Third, we compute the distance between the feature vectors obtained from the above step and produce a difference map. Finally, a simple clustering method used on the difference map can even obtain satisfied change map. Experiments carried out on typical optical aerial image data sets validate that the proposed approach compares favorably to the state-of-the-art unsupervised methods.
机译：考虑到缺乏用于监督变更检测任务的标签培训数据集，在这封信中，我们尝试通过提出具有新设计的损失功能的卷积神经网络（CNN）基于变化检测方法来减轻这个问题来实现转移学习不同的数据集。为了实现这一目标，我们首先通过采用用于监督的语义分割任务的相对足够的训练数据的优点来预留在开源数据上的U-Net模型。然后，我们最小化熟练的设计损失功能，以将从预磨模的模型提取的高级特征和包含在变化检测数据集中的语义信息，通过该损失功能，通过该变化检测数据集中的语义信息实现了传输学习。第三，我们计算从上述步骤获得的特征向量之间的距离并产生差异图。最后，差异图上使用的简单聚类方法甚至可以获得满意的变化图。在典型的光学空中图像数据集上进行的实验验证了所提出的方法对最先进的无监督方法比较。

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