PolSAR Image Classification Based on Low-Frequency and Contour Subbands-Driven Polarimetric SENet

PolSAR Image Classification Based on Low-Frequency and Contour Subbands-Driven Polarimetric SENet

In order to more efficiently mine the features of PolSAR images and build a more suitable classification model that combines the features of the polarimetric domain and the spatial domain, this article proposes a PolSAR image classification method, called low-frequency and contour subbands-driven po...

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Bibliographic Details
Main Authors: Rui Qin, Xiongjun Fu, Ping Lang
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Convolutional neural network (CNN)
nonsub- sampled laplacian pyramid (NSLP)
polarimetric synthetic aperture radar (PolSAR) image classification
polarimetric feature
spatial domain
squeeze-and-excitation (SE) network
Online Access:https://ieeexplore.ieee.org/document/9165101/

Internet

https://ieeexplore.ieee.org/document/9165101/

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