Self Attention in U-Net for Semantic Segmentation of Low Resolution SAR Images
Author:
Affiliation:
1. Dept.of Data Science and Computer Applications, Manipal Academy of Higher Education, India
2. Dept. of Information and Communication Technology, Manipal Academy of Higher Education, India
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3596286.3596291
Reference33 articles.
1. SAR imagery segmentation by statistical region growing and hierarchical merging
2. Image segmentation by contextual region growing based on fuzzy classification
3. Liang-Chieh Chen , George Papandreou , Florian Schroff , and Hartwig Adam . 2017. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 ( 2017 ). Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam. 2017. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017).
4. Information Content of Very-High-Resolution SAR Images: Semantics, Geospatial Context, and Ontologies
5. SAR Image Classification Through Information-Theoretic Textural Features, MRF Segmentation, and Object-Oriented Learning Vector Quantization
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