MAMI-CD: Multistage Attention Network for Change Detection With Mixed Feature Interaction

Abstract

Change detection (CD) from multitemporal remote sensing (RS) images plays a crucial role in various fields. Motivated by the advancements in deep learning within computer vision, numerous deep learning-based methodologies have been proposed for CD. However, the intricate backgrounds inherent in RS images present challenges to CD algorithms, leaving a notable disparity from practical applications. The changed regions between RS images frequently related to salient targets, yet encoding modules tend to forfeit the spatial information of these targets and disregard their spatial correlations during feature interaction. Consequently, leveraging the spatial correlation among targets emerges as a pivotal concern for CD algorithms. In this article, a multistage spatial attention network with mixed feature interaction (MAMI-CD) is proposed, which introduces two novel modules, i.e., a multistage attention module (MAM) and a mixed feature interaction module (MIM). The MAM extracts spatial attention features from encoding stages and transfers them to the decoding stages, thereby preserving the spatial information of targets while mitigating the influence of background noise. The MIM employs a channel-wise feature interaction instead of the traditional direct concatenation operation, enhancing the correlations between targets. MAMI-CD has been experimentally evaluated on three public datasets. On the LEVIR-CD, S2Looking, and CDD datasets, our method outperformed the state-of-the-art by 0.65%, 0.93%, and 3.16% F1 score, respectively, thus affirming the beneficial impacts of spatial correlations in CD tasks.

Keywords

• Change detection (CD)
• mixed feature interaction
• multistage attention
• remote sensing (RS) image