BUILDING DETECTION FROM SAR IMAGES USING UNET DEEP LEARNING METHOD

9 citations as recorded by crossref.

1. Urban area identification from Polarimetric Synthetic Aperture Radar data using a deep convolutional neural network I. Hammouda et al. https://doi.org/10.1080/01431161.2026.2668093
2. An improved self-training network for building and road extraction in urban areas by integrating optical and radar remotely sensed data R. Naanjam & F. Farnood Ahmadi https://doi.org/10.1007/s12145-024-01270-1
3. Deep-Learning Integration of CNN–Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection A. Noori et al. https://doi.org/10.3390/app15147770
4. Performance analysis of rule-based classification and deep learning method for automatic road extraction Z. BAYRAMOĞLU & M. UZAR https://doi.org/10.26833/ijeg.1062250
5. A Novel Pseudo-Siamese Fusion Network for Enhancing Semantic Segmentation of Building Areas in Synthetic Aperture Radar Images M. Liao et al. https://doi.org/10.3390/app15052339
6. Detection of degraded forests in Guinea, West Africa, using convolutional neural networks and Sentinel-2 time series A. Vo Quang et al. https://doi.org/10.3389/frsen.2025.1538808
7. Assessment of Deep Learning Based Image Segmentation for Identifying Floating Net Cages from Very High-Resolution Capella Synthetic Aperture Radar (SAR) Data S. Arjasakusuma & S. Kusuma https://doi.org/10.1007/s12524-024-02022-w
8. Exploring Models and Band Selection for Improved Contrail Detection with Deep Learning A. Rahmatulloh et al. https://doi.org/10.12720/jait.15.6.682-692
9. Building Detection Under Forest Canopy Using Physically Interpretable SAR Features and Hybrid Machine Learning Across Multiple Forest Biomes D. Nazyrova et al. https://doi.org/10.3390/info17080759