UniRSCD: A Unified Novel Architectural Paradigm for Remote Sensing Change Detection

TL;DR

UniRSCD introduces a unified architecture for remote sensing change detection that effectively handles various tasks and output granularities, eliminating the need for specialized decoders and achieving state-of-the-art results across multiple datasets.

Contribution

This paper presents a novel unified framework for remote sensing change detection that integrates multiple tasks into a single architecture using a frequency change prompt generator and hierarchical feature interaction.

Findings

Achieves top performance on five diverse datasets.

Effectively handles multiple change detection tasks with a single model.

Eliminates the need for specialized decoders in change detection architectures.

Abstract

In recent years, remote sensing change detection has garnered significant attention due to its critical role in resource monitoring and disaster assessment. Change detection tasks exist with different output granularities such as BCD, SCD, and BDA. However, existing methods require substantial expert knowledge to design specialized decoders that compensate for information loss during encoding across different tasks. This not only introduces uncertainty into the process of selecting optimal models for abrupt change scenarios (such as disaster outbreaks) but also limits the universality of these architectures. To address these challenges, this paper proposes a unified, general change detection framework named UniRSCD. Building upon a state space model backbone, we introduce a frequency change prompt generator as a unified encoder. The encoder dynamically scans bitemporal global context information while integrating high-frequency details with low-frequency holistic information, thereby eliminating the need for specialized decoders for feature compensation. Subsequently, the unified decoder and prediction head establish a shared representation space through hierarchical feature interaction and task-adaptive output mapping. This integrating various tasks such as binary change detection and semantic change detection into a unified architecture, thereby accommodating the differing output granularity requirements of distinct change detection tasks. Experimental results demonstrate that the proposed architecture can adapt to multiple change detection tasks and achieves leading performance on five datasets, including the binary change dataset LEVIR-CD, the semantic change dataset SECOND, and the building damage assessment dataset xBD.