OBJECT-BASED FOREST CHANGE DETECTION USING HIGH RESOLUTION SATELLITE IMAGES

10 citations as recorded by crossref.

1. Automated retrieval of forest structure variables based on multi-scale texture analysis of VHR satellite imagery B. Beguet et al. https://doi.org/10.1016/j.isprsjprs.2014.07.008
2. Improving change detection using conditional discriminative adversarial regularization L. Wang et al. https://doi.org/10.1007/s00138-025-01769-7
3. Exchanging Dual-Encoder–Decoder: A New Strategy for Change Detection With Semantic Guidance and Spatial Localization S. Zhao et al. https://doi.org/10.1109/TGRS.2023.3327780
4. The Effect of Spatial Scale on the Prediction of Tropical Forest Attributes from Image Texture J. Gallardo-Cruz et al. https://doi.org/10.1155/2024/7178211
5. GPR Bscan Change Detection Network for Structural Defect Evolution Q. Xu et al. https://doi.org/10.1109/TGRS.2024.3480122
6. Detection of Forest Area Density using Satellite Images A. S et al. https://doi.org/10.1016/j.procs.2025.04.319
7. Network and Dataset for Multiscale Remote Sensing Image Change Detection S. Liu et al. https://doi.org/10.1109/JSTARS.2024.3522135
8. Forest Cover Change Monitoring Using Sub-Pixel Mapping with Edge-Matching Correction S. Xia et al. https://doi.org/10.3390/f14091776
9. A Deep Learning Approach for Calamity Assessment Using Sentinel-2 Data D. Scharvogel et al. https://doi.org/10.3390/f11121239
10. Object-based characterization of vegetation heterogeneity with sentinel images proves efficient in a highly human-influenced National Park of Côte d’Ivoire S. Houphlet et al. https://doi.org/10.1007/s10661-022-10792-4