A practically feasible transfer learning method for deep-temporal urban change monitoring
Author:
Affiliation:
1. IT4Innovations, VSB – Technical University, Ostrava, Czech Republic
Funder
Ministry of Education, Youth and Sports of the Czech Republic
Publisher
Informa UK Limited
Subject
General Earth and Planetary Sciences
Link
https://www.tandfonline.com/doi/pdf/10.1080/01431161.2023.2243021
Reference71 articles.
1. SENECA: Change detection in optical imagery using Siamese networks with Active-Transfer Learning
2. Distilling Before Refine: Spatio-Temporal Transfer Learning for Mapping Irrigated Areas Using Sentinel-1 Time Series
3. A Mixed Markov model for change detection in aerial photos with large time differences
4. Change Detection in Optical Aerial Images by a Multilayer Conditional Mixed Markov Model
5. Bagging predictors
Cited by 2 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. Monitoring of Urban Changes With Multimodal Sentinel 1 and 2 Data in Mariupol, Ukraine, in 2022/23;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024
2. Correction;International Journal of Remote Sensing;2023-09-02
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