MVDC: A Multi-View Domain Confusion Strategy for Cross Domain Change Detection

Remote sensing (RS) change detection (CD) refers to the difference of RS images at different times in the same geographic area. The method of using high-resolution RS images based on deep learning (DL) has the problem of poor domain adaptation (DA) under geographical isolation conditions, especially in the cropland scene with different phenology and planting type. Based on the universal Siamese network architecture, a multi-view domain confusion (MVDC) module is designed. The module performs discriminant operations on the generated source domain features and target, and guides the generator to generate more domain invariant features. The time consistency information is taken into account while the difference between geographical regions is reduced. The module can theoretically be plugged into any universal CD network. The experimental results show that the adaptive ability of multiple CD methods is improved after the addition of MVDC.