Attentive Dual Stream Siamese U-Net for Flood Detection on Multi-Temporal Sentinel-1 Data-Reference-Cited by-同舟云学术

Attentive Dual Stream Siamese U-Net for Flood Detection on Multi-Temporal Sentinel-1 Data

Published:2022-07-17 Issue: Volume: Page:
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Container-title:IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Yadav Ritu¹,Nascetti Andrea¹,Ban Yifang¹

Affiliation:

1. KTH Royal Institute of Technology,Division of Geoinformatics,Sweden

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9883023/9883024/09883132.pdf?arnumber=9883132

Reference11 articles.

1. Learning Deep Models from Weak Labels for Water Surface Segmentation in Sar Images

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3. Google Earth Engine: Planetary-scale geospatial analysis for everyone

4. Water Body Detection using Deep Learning with Sentinel-1 SAR satellite data and Land Cover Maps

5. Concur-rent spatial and channel 'squeeze & excitation in fully convolutional networks;roy;International Conference on Medical Image Computing and Computer-Assisted Intervention,2018

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3. Self-Attention Driven Decoder for SAR Image-based Semantic Flood Zone Segmentation;Proceedings of the 6th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery;2023-11-13

4. An Extremely Lightweight U-Net with Soft Fusion for Flood Detection Using Multi-Source Satellite Images;IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium;2023-07-16

5. Attentive decoder network for flood analysis using sentinel 1 images;2023 International Conference on Communication, Circuits, and Systems (IC3S);2023-05-26