Exchanging Dual Encoder-Decoder: A New Strategy for Change Detection
  with Semantic Guidance and Spatial Localization
Sijie Zhao Xueliang Zhang Pengfeng Xiao Guangjun He
Change detection is a critical task in earth observation applications.
Recently, deep learning-based methods have shown promising performance and are
quickly adopted in change detection. However, the widely used multiple encoder
and single decoder (MESD) as well as dual encoder-decoder (DED) architectures
still struggle to effectively handle change detection well. The former has
problems of bitemporal feature interference in the feature-level fusion, while
the latter is inapplicable to intraclass change detection and multiview
building change detection. To solve these problems, we propose a new strategy
with an exchanging dual encoder-decoder structure for binary change detection
with semantic guidance and spatial localization. The proposed strategy solves
the problems of bitemporal feature inference in MESD by fusing bitemporal
features in the decision level and the inapplicability in DED by determining
changed areas using bitemporal semantic features. We build a binary change
detection model based on this strategy, and then validate and compare it with
18 state-of-the-art change detection methods on six datasets in three
scenarios, including intraclass change detection datasets (CDD, SYSU),
single-view building change detection datasets (WHU, LEVIR-CD, LEVIR-CD+) and a
multiview building change detection dataset (NJDS). The experimental results
demonstrate that our model achieves superior performance with high efficiency
and outperforms all benchmark methods with F1-scores of 97.77%, 83.07%, 94.86%,
92.33%, 91.39%, 74.35% on CDD, SYSU, WHU, LEVIR-CD, LEVIR- CD+, and NJDS
datasets, respectively. The code of this work will be available at
https://github.com/NJU-LHRS/official-SGSLN.