XFIMNet: an Explainable deep learning architecture for versatile flood inundation mapping with synthetic aperture radar and multi-spectral optical images-Reference-Cited by-同舟云学术

XFIMNet: an Explainable deep learning architecture for versatile flood inundation mapping with synthetic aperture radar and multi-spectral optical images

Published:2023-12-17 Issue:24 Volume:44 Page:7755-7789
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Sanderson Jacob¹,Tengtrairat Naruephorn²,Woo Wai Lok¹,Mao Hua¹,Al-Nima Raid Rafi³

Affiliation:

1. Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK

2. School of Software Engineering, Payap University, Chiang Mai, Thailand

3. Technical Engineering College, Northern Technical University, Mosul, Nineveh Governorate, Iraq

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2023.2288945

Reference60 articles.

1. A New Statistical-Based Kurtosis Wavelet Energy Feature for Texture Recognition of SAR Images

2. A New Region-Based Active Contour Model with Skewness Wavelet Energy for Segmentation of SAR Images

3. H2O-Net: Self-Supervised Flood Segmentation via Adversarial Domain Adaptation and Label Refinement

4. Flood Hazard and Risk Mapping by Applying an Explainable Machine Learning Framework Using Satellite Imagery and GIS Data

5. Sen1Floods11: a georeferenced dataset to train and test deep learning flood algorithms for Sentinel-1

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