Optical and SAR data integration for automatic change pattern detection

7 citations as recorded by crossref.

1. Methods in the spatial deep learning: current status and future direction B. Mishra et al. https://doi.org/10.1007/s41324-021-00425-2
2. Monitoring chlorophyll-a in Phewa Lake, Nepal using satellite images and ensemble-based learning P. Joshi et al. https://doi.org/10.3389/fenvs.2026.1717629
3. A Sentinel-1 Based Processing Chain for Detection of Cyclonic Flood Impacts C. Alexandre et al. https://doi.org/10.3390/rs12020252
4. Extraction and mapping of downpour impact and their Cumulonimbus origin, 20 May 2020, Vâlcea (Romania) via Sentinnel-1 SAR dual polarization K. Hachemi et al. https://doi.org/10.1038/s41598-022-22909-3
5. MSCDUNet: A Deep Learning Framework for Built-Up Area Change Detection Integrating Multispectral, SAR, and VHR Data H. Li et al. https://doi.org/10.1109/JSTARS.2022.3181155
6. A review of fusion framework using optical sensors and Synthetic Aperture Radar imagery to detect and map land degradation and sustainable land management in the semi-arid regions D. Sengani et al. https://doi.org/10.1080/10106049.2023.2278325
7. Post-War Urban Damage Mapping Using InSAR: The Case of Mosul City in Iraq A. Boloorani et al. https://doi.org/10.3390/ijgi10030140