Deforestation change detection using high-resolution multi-temporal X-Band SAR images and supervised learning classification-Reference-Cited by-同舟云学术

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Deforestation change detection using high-resolution multi-temporal X-Band SAR images and supervised learning classification

Published:2016-07 Issue: Volume: Page:
ISSN:
Container-title:2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
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Author:

Barreto Thiago L. M.,Rosa Rafael A. S.,Wimmer Christian,Nogueira Joao B.,Almeida Jurandy,Cappabianco Fabio Augusto Menocci

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/7592514/7728980/07730355.pdf?arnumber=7730355

Cited by 11 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. Tech-Driven Forest Conservation: Combating Deforestation With Internet of Things, Artificial Intelligence, and Remote Sensing;IEEE Internet of Things Journal;2024-07-15

2. Time-referenced wavelet spatio-temporal change detection on multi-temporal SAR images;2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS);2024-04-08

3. Multicue Contrastive Self-Supervised Learning for Change Detection in Remote Sensing;IEEE Transactions on Geoscience and Remote Sensing;2023

4. Wavelet Spatio-Temporal Change Detection on Multitemporal SAR Images;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2023

5. Multi-Scale Attention based Transformer U-NET for Change Detection;IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium;2022-07-17

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