Stable Prototype-Guided Single-Temporal Supervised Learning for Change Detection and Extraction of Building

Abstract

Change detection and extraction of buildings based on convolutional neural networks (CNNs) have made encouraging progress in the remote sensing community. Although these two tasks are different in objective and application scenarios, both focus on building objects. However, previous methods were accustomed to considering these two tasks separately, and the change detection task suffered from the precondition that bitemporal labeled images were used as paired supervision signals. In this study, we propose a stable prototype guided single-temporal supervised learning framework (PGLF) as a joint solution for building change detection and cross-temporal extraction by exploring two cores: knowledge commonality and task specificity. For knowledge commonality, we introduced a multi-prototype representation module (MPRM) to generate stable building prototypes from support foreground features and designed a prototype to query feature adaptive fusion (PQAF) module to suppress background noise and extract discriminative building features in a way that support prototypes-guided query feature enhancement. For task specificity, we designed a multiscale spatiotemporal interaction module (MSTI) to capture bidirectional change features with strong spatiotemporal correlations. Besides, we developed a pseudo bitemporal image pair construction method to improve the performance of building change detection and cross-temporal extraction under single-temporal supervision signals. PGLF was trained on pseudo bitemporal labeled image pairs and tested on the public aerial WHU building dataset and proposed satellite SPO building dataset. The comprehensive experimental results demonstrate the superiority of the proposed method.