Wetlands Classification Using Quad-Polarimetric Synthetic Aperture Radar through Convolutional Neural Networks Based on Polarimetric Features

Wetlands Classification Using Quad-Polarimetric Synthetic Aperture Radar through Convolutional Neural Networks Based on Polarimetric Features

Wetlands are the “kidneys” of the earth and are crucial to the ecological environment. In this study, we utilized GF-3 quad-polarimetric synthetic aperture radar (QP) images to classify the ground objects (nearshore water, seawater, spartina alterniflora, tamarix, reed, tidal flat, and suaeda salsa)...

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Bibliographic Details
Main Authors: Shuaiying Zhang, Wentao An, Yue Zhang, Lizhen Cui, Chunhua Xie
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Remote Sensing
Subjects:
polarization coherency matrix
reflection symmetry decomposition (RSD)
polarimetric features
four schemes
noise test
wetland classification
Online Access:https://www.mdpi.com/2072-4292/14/20/5133

Internet

https://www.mdpi.com/2072-4292/14/20/5133

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