Full-Learning Rotational Quaternion Convolutional Neural Networks and Confluence of Differently Represented Data for PolSAR Land Classification
Author:
Affiliation:
1. Department of Electrical Engineering and Information Systems, The University of Tokyo, Bunkyo-ku, Japan
Funder
Japan Society for the Promotion of Science
Research Institute of Electrical Communication
Tohoku University
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Atmospheric Science,Computers in Earth Sciences
Link
http://xplorestaging.ieee.org/ielx7/4609443/9656571/09755119.pdf?arnumber=9755119
Reference28 articles.
1. Impact of Reducing Polarimetric SAR Input on the Uncertainty of Crop Classifications Based on the Random Forests Algorithm
2. Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features
3. Polarimetric SAR Image Classification Using Deep Convolutional Neural Networks
4. Complex-Valued Convolutional Neural Network and Its Application in Polarimetric SAR Image Classification
5. A Novel Technique Based on Deep Learning and a Synthetic Target Database for Classification of Urban Areas in PolSAR Data
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