Learning Oriented Region-based Convolutional Neural Networks for Building Detection in Satellite Remote Sensing Images

18 citations as recorded by crossref.

1. Yüksek çözünürlüklü uydu görüntülerinden daha hızlı bölge tabanlı derin öğrenme modeli ile bina tespiti E. SARALIOĞLU & O. GÜNGÖR https://doi.org/10.17714/gumusfenbil.1012519
2. CF2PN: A Cross-Scale Feature Fusion Pyramid Network Based Remote Sensing Target Detection W. Huang et al. https://doi.org/10.3390/rs13050847
3. Arbitrary-Oriented Vehicle Detection in Aerial Imagery with Single Convolutional Neural Networks T. Tang et al. https://doi.org/10.3390/rs9111170
4. Analysis on Saliency Estimation Methods in High-Resolution Optical Remote Sensing Imagery for Multi-Scale Ship Detection Z. Li et al. https://doi.org/10.1109/ACCESS.2020.3033469
5. Depth-enhanced feature pyramid network for occlusion-aware verification of buildings from oblique images Q. Zhu et al. https://doi.org/10.1016/j.isprsjprs.2021.01.025
6. Orientation Robust Object Detection in Aerial Images Based on R-NMS Q. Liu & J. Li https://doi.org/10.1016/j.procs.2019.06.102
7. Building Detection using Two-Layered Novel Convolutional Neural Networks P. Karuppusamy https://doi.org/10.36548/jscp.2021.1.004
8. Land use and land cover classification with hyperspectral data: A comprehensive review of methods, challenges and future directions M. Moharram & D. Sundaram https://doi.org/10.1016/j.neucom.2023.03.025
9. Building Extraction and Number Statistics in WUI Areas Based on UNet Structure and Ensemble Learning D. Chen et al. https://doi.org/10.3390/rs13061172
10. Classified 3D mapping and deep learning-aided signal power estimation architecture for the deployment of wireless communication systems Y. Egi & E. Eyceyurt https://doi.org/10.1186/s13638-022-02188-2
11. Learning a two-stage CNN model for multi-sized building detection in remote sensing images C. Chen et al. https://doi.org/10.1080/2150704X.2018.1528398
12. Object Detection in Remote Sensing Images Based on a Scene-Contextual Feature Pyramid Network C. Chen et al. https://doi.org/10.3390/rs11030339
13. Evaluation of Deep Learning CNN Model for Land Use Land Cover Classification and Crop Identification Using Hyperspectral Remote Sensing Images K. Bhosle & V. Musande https://doi.org/10.1007/s12524-019-01041-2
14. Capturing heterogeneous urban growth using SLEUTH model A. Saxena & M. Jat https://doi.org/10.1016/j.rsase.2018.12.012
15. Deformable ConvNet with Aspect Ratio Constrained NMS for Object Detection in Remote Sensing Imagery Z. Xu et al. https://doi.org/10.3390/rs9121312
16. Comparative Research on Deep Learning Approaches for Airplane Detection from Very High-Resolution Satellite Images U. Alganci et al. https://doi.org/10.3390/rs12030458
17. Artificial intelligence inspired IoT-fog based framework for generating early alerts while train passengers traveling in dangerous states using surveillance videos M. Saini et al. https://doi.org/10.1007/s11042-023-16107-0
18. A Refined and Efficient CNN Algorithm for Remote Sensing Object Detection B. Liu et al. https://doi.org/10.3390/s24227166