Automatic Mapping of Tropical Cyclone-Induced Coastal Inundation in SAR Imagery Based on Clustering of Deep Features

Researchers have already verified that the deep learning (DL) technology can realize accurate and robust mapping of tropical cyclone-induced coastal inundation in synthetic aperture radar imagery. In order to liberate the DL-based inundation mapping from human supervision, we propose to use the clus...

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Veröffentlicht in:IEEE International Geoscience and Remote Sensing Symposium proceedings S. 5765 - 5768
Hauptverfasser: Liu, Bin, Li, Xiaofeng, Zheng, Gang
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 26.09.2020
Schlagworte:
clustering
Coastal inundation mapping
deep convolutional autoen-coder (DCAE)
Feature extraction
Floods
Radar imaging
Radar polarimetry
Sea measurements
Sensors
Synthetic aperture radar
synthetic aperture radar (SAR) imagery
ISSN:2153-7003
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Zusammenfassung:Researchers have already verified that the deep learning (DL) technology can realize accurate and robust mapping of tropical cyclone-induced coastal inundation in synthetic aperture radar imagery. In order to liberate the DL-based inundation mapping from human supervision, we propose to use the clustering of deep convolutional autoencoder-generated features. The mapping results of Lekima 2019-induced inundation demonstrate the advantages and availability of the proposed method.
ISSN:2153-7003
DOI:10.1109/IGARSS39084.2020.9324529