Open Access
Issue |
ITM Web Conf.
Volume 70, 2025
2024 2nd International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2024)
|
|
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Article Number | 03023 | |
Number of page(s) | 8 | |
Section | Image Processing and Computer Vision | |
DOI | https://doi.org/10.1051/itmconf/20257003023 | |
Published online | 23 January 2025 |
- A. Temenos, N. Temenos, M. Kaselimi, A. Doulamis, N. Doulamis, 2023. Interpretable Deep Learning Framework for Land Use and Land Cover Classification in Remote Sensing Using SHAP. IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1-5, Art no. 8500105. [Google Scholar]
- T. Loran, A. Barros Cardoso da Silva, S. K. Joshi, S. V. Baumgartner, G. Krieger, 2023. Ship Detection Based on Faster R-CNN Using Range-Compressed Airborne Radar Data. IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1-5, Art no. 3500205. [Google Scholar]
- I. Kotaridis, M. Lazaridou, 2021. Remote Sensing Image Segmentation Advances: A Meta-Analysis. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 173, pp. 309-322 [CrossRef] [Google Scholar]
- F.Q.L. Feng, B.A. Chen, G.Q. Li, X.C. Yao, B.B. Gao, L.C. Zhang, 2022. A Review for Sample Datasets of Remote Sensing Imagery. National Remote Sensing Bulletin, vol. 26(4), pp. 589-605. [CrossRef] [Google Scholar]
- J. Long, E. Shelhamer, T. Darrell, 2015. Fully Convolutional Networks for Semantic Segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431-3440. SCITEPRESS. [Google Scholar]
- O. Ronneberger, P. Fischer, T. Brox, 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III. Springer International Publishing, pp. 234-241. [Google Scholar]
- T. Heimann, et al., 2009. Comparison and Evaluation of Methods for Liver Segmentation from CT Datasets. IEEE Transactions on Medical Imaging, vol. 28(8), pp. 1251-1265. [CrossRef] [Google Scholar]
- J. Wang, Z. Zheng, A. Ma, et al., 2021. LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation. arXiv preprint arXiv:2110.08733. [Google Scholar]
- T. Sun, et al., 2019. Leveraging Crowdsourced GPS Data for Road Extraction from Aerial Imagery. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. SCITEPRESS. [Google Scholar]
- J. Long, E. Shelhamer, T. Darrell, 2015. Fully Convolutional Networks for Semantic Segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431-3440. SCITEPRESS. [Google Scholar]
- O. Ronneberger, P. Fischer, T. Brox, 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234-241. SCITEPRESS. [Google Scholar]
- V. Badrinarayanan, A. Kendall, R. Cipolla, 2017. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39(12), pp. 2481-2495. [CrossRef] [PubMed] [Google Scholar]
- B. Baheti, et al., 2020. Eff-U-Net: A Novel Architecture for Semantic Segmentation in Unstructured Environment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. [Google Scholar]
- S.S. Agaian, K. Panetta, A.M. Grigoryan, 2001. Transform-Based Image Enhancement Algorithms with Performance Measure. IEEE Transactions on Image Processing, vol. 10(3), pp. 367-382. [CrossRef] [Google Scholar]
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