Full-Learning Rotational Quaternion Convolutional Neural Networks and Confluence of Differently Represented Data for PolSAR Land Classification

Full-Learning Rotational Quaternion Convolutional Neural Networks and Confluence of Differently Represented Data for PolSAR Land Classification

Quaternion convolutional neural networks (QCNNs) expand the range of their applications in processing optical and polarimetric synthetic aperture radar (PolSAR) images. Conventional real-valued convolutional neural networks (RVCNNs) compress a three-channel input image into a single-channel feature...

Full description

Bibliographic Details
Main Authors: Yuya Matsumoto, Ryo Natsuaki, Akira Hirose
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Convolutional neural network (CNN)
polarimetric synthetic aperture radar (PolSAR)
quaternion neural network (QNN)
Online Access:https://ieeexplore.ieee.org/document/9755119/

Internet

https://ieeexplore.ieee.org/document/9755119/

Similar Items