Bridging Supervision Gaps: A Unified Framework for Remote Sensing Change Detection

• URL: http://arxiv.org/abs/2601.17747v1
• Date: Sun, 25 Jan 2026 08:43:29 GMT
• Title: Bridging Supervision Gaps: A Unified Framework for Remote Sensing Change Detection
• Authors: Kaixuan Jiang, Chen Wu, Zhenghui Zhao, Chengxi Han,
• Abstract summary: UniCD collaboratively handles supervised, weakly-supervised, and unsupervised tasks.<n>UniCD eliminates architectural barriers through a shared encoder and multi-branch collaborative learning mechanism.<n>Experiments on mainstream datasets demonstrate that UniCD achieves optimal performance across three tasks.
• Score: 2.7351165166984845
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Change detection (CD) aims to identify surface changes from multi-temporal remote sensing imagery. In real-world scenarios, Pixel-level change labels are expensive to acquire, and existing models struggle to adapt to scenarios with diverse annotation availability. To tackle this challenge, we propose a unified change detection framework (UniCD), which collaboratively handles supervised, weakly-supervised, and unsupervised tasks through a coupled architecture. UniCD eliminates architectural barriers through a shared encoder and multi-branch collaborative learning mechanism, achieving deep coupling of heterogeneous supervision signals. Specifically, UniCD consists of three supervision-specific branches. In the supervision branch, UniCD introduces the spatial-temporal awareness module (STAM), achieving efficient synergistic fusion of bi-temporal features. In the weakly-supervised branch, we construct change representation regularization (CRR), which steers model convergence from coarse-grained activations toward coherent and separable change modeling. In the unsupervised branch, we propose semantic prior-driven change inference (SPCI), which transforms unsupervised tasks into controlled weakly-supervised path optimization. Experiments on mainstream datasets demonstrate that UniCD achieves optimal performance across three tasks. It exhibits significant accuracy improvements in weakly and unsupervised scenarios, surpassing current state-of-the-art by 12.72% and 12.37% on LEVIR-CD, respectively.

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