2019IEEE Transactions of Geoscience and Remote Sensing武大变化检测新模型：DSFA
1.论文地址：Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images
摘要:Change detection has been a hotspot in remote sensing technology for a long time. With the increasing availability of multi-temporal remote sensing images, numerous change detection algorithms have been proposed. Among these methods, image transformation methods with feature extraction and mapping could effectively highlight the changed information and thus has better change detection performance. However, changes of multi-temporal images are usually complex, existing methods are not effective enough. In recent years, deep network has shown its brilliant performance in many fields including feature extraction and projection. Therefore, in this paper, based on deep network and slow feature analysis (SFA) the