Change detection (CD) networks based on supervised learning have been used in diverse CD tasks. However, such supervised CD networks require a large amount of data and only use information from current images. In addition, it is time consuming to manually acquire the ground truth data for newly obtained images. Here, we proposed a novel method for CD in case of a lack of training data in an area near by another one with the available ground truth data. The proposed method automatically entails generating training data and fine-tuning the CD network. To detect changes in target images without ground truth data, the difference images were generated using spectral similarity measure, and the training data were selected via fuzzy c-means clustering. Recurrent fully convolutional networks with multiscale three-dimensional filters were used to extract objects of various sizes from unmanned aerial vehicle (UAV) images. The CD network was pre-trained on labeled source domain data; then, the network was fine-tuned on target images using generated training data. Two further CD networks were trained with a combined weighted loss function. The training data in the target domain were iteratively updated using he prediction map of the CD network. Experiments on two hyperspectral UAV datasets confirmed that the proposed method is capable of transferring change rules and improving CD results based on training data extracted in an unsupervised way.

中文翻译：

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循环完全卷积网络的传输变化规则，用于无地面真实数据的高光谱无人机图像

基于监督学习的变更检测（CD）网络已用于各种CD任务中。但是，这种受监督的CD网络需要大量数据，并且仅使用来自当前映像的信息。另外，手动获取新获得的图像的地面真实数据很费时间。在这里，我们提出了一种新的CD方法，以防在缺乏另一个具有可用地面真实数据的区域附近的训练数据的情况下。所提出的方法自动需要生成训练数据并微调CD网络。为了检测没有地面真实数据的目标图像的变化，使用光谱相似性度量生成差异图像，并通过模糊c均值聚类选择训练数据。具有多尺度三维滤波器的循环全卷积网络用于从无人机图像中提取各种大小的物体。CD网络已在标记的源域数据上进行了预培训；然后，使用生成的训练数据对目标图像进行微调。使用组合的加权损失函数训练了另外两个CD网络。使用CD网络的预测图迭代更新目标域中的训练数据。在两个高光谱无人机数据集上进行的实验证实，该方法能够以无人监督的方式提取训练数据，从而传递变更规则并改善CD结果。使用生成的训练数据对目标图像进行了微调。使用组合的加权损失函数训练了另外两个CD网络。使用CD网络的预测图迭代更新目标域中的训练数据。在两个高光谱无人机数据集上进行的实验证实，该方法能够以无人监督的方式提取训练数据，从而传递变更规则并改善CD结果。使用生成的训练数据对目标图像进行了微调。使用组合的加权损失函数训练了另外两个CD网络。使用CD网络的预测图迭代更新目标域中的训练数据。在两个高光谱无人机数据集上进行的实验证实，该方法能够以无人监督的方式提取训练数据，从而传递变更规则并改善CD结果。