MFFNet: a building change detection method based on fusion of spectral and geometric information

Abstract

Accurate detection and extraction of changes in buildings heights is important in monitoring construction (both legal and illegal) and assessing disasters. It is also important information for updating real 3D scenes. However, when using remote sensing images, shadows, vegetation and objects with similar spectral and morphological characteristics as buildings can cause false detections, omissions and incomplete patch edges. To address this issue, we develop the multiscale feature fusion network for dual-modal data (MFFNet), which has two main aspects: (1) The multi-dual-modal feature fusion module detects changes in features with similar spectral and morphological characteristics as buildings. This mitigates false detections by making the model more aware of areas where the elevation has changed over time. (2) Because building extraction is affected by shadows and vegetation, we designed a multiscale feature shuffle module. It takes multiscale features and establishes relationships between neighbouring pixels using the pixel-shuffle algorithm, then fuses and reorganizes the multiscale features to highlight the relationships between global contexts, thereby mitigating the problem of building occlusion by shadows. Comparative experiments show that MFFNet achieves better results on GF7-CD and 3DCD datasets than other similar methods. The proposed method can more accurately monitor building changes over large areas.

Keywords:

• Building change detection
• digital surface model
• dual-modal data fusion
• multiscale feature shuffle

Authors’ contributions

Conceptualization, Z.H.G. and J.P.P.; methodology, Z.H.G. and J.P.P. and P.X; validation, Z.H.G.; formal analysis P.X. and C.Q and X.X.W and Y.H.Y and Y.W and L.Z; resources, H.J.Z. and H.Z.R.; writing—original draft preparation, Z.H.G.; writing—review and editing, J.P.P. and Z.H.G.; All authors have read and agreed to the published version of the manuscript.

Acknowledgement

We thank the authors of the 3DCD dataset.

Disclosure statement

The authors declare no conflict of interest. And we sincerely appreciate that academic editors and reviewers give their help comments and suggestions.

Additional information

Funding

This work is supported by the Key R&D Program of Ningxia Autonomous Region: Ecological environment monitoring and platform development of ecological barrier protection system for Helan Mountain(2022CMG02014)and Chongqing Graduate Joint Training Base Construction Project (JDLHPYJD2019004) and The Project Supported by the Open Fund of Key Laboratory of Monitoring, Evaluation and Early Warning of Territorial Spatial Planning Implementation, Ministry of Natural Resources(No: LMEE-KF2023001).