Random Region Matting for the High-Resolution PolSAR Image Semantic Segmentation

Random Region Matting for the High-Resolution PolSAR Image Semantic Segmentation

Polarimetric synthetic aperture radar (PolSAR) imagery can provide more intuitive and detailed SAR polarization information, and it is widely used in the classification and semantic segmentation of remote sensing. To bridge the PolSAR data and application, the 2020 Gaofen Challenge on Automated High...

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Bibliographic Details
Main Authors: Jun Ni, Fan Zhang, Fei Ma, Qiang Yin, Deliang Xiang
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Data augmentation
DeepLabV3<inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX">$+$</tex-math> </inline-formula>
Gaofen-3
image classification
polarimetric synthetic aperture radar (PolSAR)
semantic segmentation
Online Access:https://ieeexplore.ieee.org/document/9364362/

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