TOPICBERT:BUILDING CHANGE DETECTION USING ATTENTION-  BASED FEATURES (AFDE-Net) SATELLITE IMAGE DIFFERENTIAL  ENHANCEMENT

Mrs.M.MOUNIKA, MATURI GAYATRI REDDY, GODISHALA SOWJANYA,  SAWDA AFREEN

TOPICBERT:BUILDING CHANGE DETECTION USING ATTENTION- BASED FEATURES (AFDE-Net) SATELLITE IMAGE DIFFERENTIAL ENHANCEMENT

Authors

Mrs.M.MOUNIKA, MATURI GAYATRI REDDY, GODISHALA SOWJANYA, SAWDA AFREEN

Keywords:

Building Change Detection, Satellite Imagery, Siamese Network, AFDE-Net, Ensemble Spatial Channel Attention Fusion (ESCAF) module, Deep Supervision (DS) module, High-resolution Multi-temporal Satellite Images, EGY-BCD Dataset, Urbanization Monitoring, Geospatial Databases

Abstract

Building change detection (BCD) from satellite imagery is critical for monitoring urbanization, managingagricultural land, and updating geospatial databases. However, complex variations in building roofs that resemblethe background of their surroundings pose challenges for deep-learning-based change detection methods due to their

References

R. Yadav, A. Nascetti and Y. Ban, "Building change detection using multi- temporal airborne LiDAR data", arXiv:2204.12535, 2022. Show in Context CrossRef Google Scholar

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Published

2024-02-15

How to Cite

Mrs.M.MOUNIKA, MATURI GAYATRI REDDY, GODISHALA SOWJANYA, SAWDA AFREEN. (2024). TOPICBERT:BUILDING CHANGE DETECTION USING ATTENTION- BASED FEATURES (AFDE-Net) SATELLITE IMAGE DIFFERENTIAL ENHANCEMENT . Journal of Computational Analysis and Applications (JoCAAA), 32(2), 369–377. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/2711