Multi-Modal Building Change Detection for Large-Scale Small Changes: Benchmark and Baseline

Abstract

Change detection in optical remote sensing imagery is susceptible to illumination fluctuations, seasonal changes, and variations in surface land-cover materials. Relying solely on RGB imagery often produces pseudo-changes and leads to semantic ambiguity in features. Incorporating near-infrared (NIR) information provides heterogeneous physical cues that are complementary to visible light, thereby enhancing the discriminability of building materials and tiny structures while improving detection accuracy. However, existing multi-modal datasets generally lack high-resolution and accurately registered bi-temporal imagery, and current methods often fail to fully exploit the inherent heterogeneity between these modalities. To address these issues, we introduce the Large-scale Small-change Multi-modal Dataset (LSMD), a bi-temporal RGB-NIR building change detection benchmark dataset targeting small changes in realistic scenarios, providing a rigorous testing platform for evaluating multi-modal change detection methods in complex environments. Based on LSMD, we further propose the Multi-modal Spectral Complementarity Network (MSCNet) to achieve effective cross-modal feature fusion. MSCNet comprises three key components: the Neighborhood Context Enhancement Module (NCEM) to strengthen local spatial details, the Cross-modal Alignment and Interaction Module (CAIM) to enable deep interaction between RGB and NIR features, and the Saliency-aware Multisource Refinement Module (SMRM) to progressively refine fused features. Extensive experiments demonstrate that MSCNet effectively leverages multi-modal information and consistently outperforms existing methods under multiple input configurations, validating its efficacy for fine-grained building change detection. The source code will be made publicly available at: https://github.com/AeroVILab-AHU/LSMD

Figures (12)

• Figure 1: Illustration of data distribution and challenging scenarios in realistic change detection. Note: unchanged images are excluded in (a) and (b) to focus on change-relevant statistics. (a) Comparison of the average change ratio per image between the proposed LSMD and other mainstream benchmarks. (b) Comparison of image proportions across different change ratios (1%--10%) between the proposed LSMD and existing benchmarks. (c) Visual examples of small-scale changes in large-scale scenes. (d) Visual examples of small buildings under vegetation backgrounds.
• Figure 2: General architectural paradigm for MCD. Synchronous multi-modal imagery is provided at each temporal phase ($t_1$ and $t_2$). The architecture employs dual-branch feature encoding and independent change perception to capture initial temporal difference cues for each modality. Finally, a Multi-modal Complementary Fusion mechanism is utilized for the synergistic integration of multi-source information, generating highly robust change detection results.
• Figure 3: Samples and annotations from the LSMD dataset. (Left) Bi-temporal RGB and NIR image pairs with ground truth. (Right) Detailed illustrations of changed regions.
• Figure 4: Overall architecture of the proposed MSCNet. First, a Siamese backbone is employed to extract bi-temporal RGB and NIR features separately. Next, the Neighborhood Context Enhancement Module (NCEM) enhances the features to capture local change information. The Cross-modal Alignment and Interaction Module (CAIM) then integrates the RGB and NIR difference features to generate a more discriminative fused representation. Subsequently, the Saliency-aware Multisource Refinement Module (SMRM) leverages high-level semantic priors generated offline by RemoteSAM and, under the guidance of semantic masks, multi-modal difference features, and multi-scale context, progressively refines the fused features to restore spatial resolution and maintain cross-scale feature consistency. Finally, the features processed by these modules are used to generate the change detection results.
• Figure 5: Structure of NCEM. Multi-level neighboring features are selectively aggregated and adaptively weighted to enhance local spatial details and contextual consistency.