A Weakly-Supervised Change Detection Technique for SAR Images Based on Deep Learning and Synthetic Training Data Generated by an Ensemble of Self-Organizing Maps
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Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/8891871/8897702/08898344.pdf?arnumber=8898344
Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Fully Convolutional Change Detection Framework With Generative Adversarial Network for Unsupervised, Weakly Supervised and Regional Supervised Change Detection;IEEE Transactions on Pattern Analysis and Machine Intelligence;2023-08
2. Synthetic Image Sequence Generation for Endothelium in Situ Simulator;2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE);2023-07-24
3. A Weakly-Supervised Change Detection for Multispectral Earth Observation Imagery using a Long Short- Term Memory Classifier with a Virtual Training Data Neural Generator;2022 14th International Conference on Communications (COMM);2022-06-16
4. Assessing the Qualities of Synthetic Visual Data Production;2021 9th International Conference on Information and Education Technology (ICIET);2021-03-27
5. CNN Hyperspectral Image Classification Using Training Sample Augmentation with Generative Adversarial Networks;2020 13th International Conference on Communications (COMM);2020-06
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