论文标题
基于深度学习的无监督的自我训练算法的光学空中图像更改检测
Unsupervised Self-training Algorithm Based on Deep Learning for Optical Aerial Images Change Detection
论文作者
论文摘要
光学天线变化检测是地球观察中的一项重要任务，并且在过去几十年中已经进行了广泛的研究。通常，具有出色性能的监督变更检测方法需要大量标记的培训数据，这些数据是通过高成本的手动注释获得的。在本文中，我们提出了一种新型的无监督的自我训练算法（USTA），用于光线空图像更改检测。传统方法（例如变更矢量分析）用于生成伪标签。我们使用这些伪标签来训练设计精良的卷积神经网络。该网络用作教师对原始的多阶段图像进行分类，以生成另一组伪标签。然后，使用两组伪标签来共同培训与老师相同结构的学生网络。最终的更改检测结果可以由训练有素的学生网络获得。此外，我们设计了一个图像过滤器，以控制网络训练过程中伪标签中变更信息的使用。算法的整个过程是无监督的过程，没有手动标记的标签。实际数据集的实验结果证明了我们提出的方法的竞争性能。
Optical aerial images change detection is an important task in earth observation and has been extensively investigated in the past few decades. Generally, the supervised change detection methods with superior performance require a large amount of labeled training data which is obtained by manual annotation with high cost. In this paper, we present a novel unsupervised self-training algorithm (USTA) for optical aerial images change detection. The traditional method such as change vector analysis is used to generate the pseudo labels. We use these pseudo labels to train a well designed convolutional neural network. The network is used as a teacher to classify the original multitemporal images to generate another set of pseudo labels. Then two set of pseudo labels are used to jointly train a student network with the same structure as the teacher. The final change detection result can be obtained by the trained student network. Besides, we design an image filter to control the usage of change information in the pseudo labels in the training process of the network. The whole process of the algorithm is an unsupervised process without manually marked labels. Experimental results on the real datasets demonstrate competitive performance of our proposed method.
