Unsupervised Complex-Valued Sparse Feature Learning for PolSAR Image Classification
Author:
Affiliation:
1. National Laboratory of Radar Signal Processing, Xidian University, Xi’an, China
2. School of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an, China
Funder
Natural Science Foundation of China
Civil Space Thirteen Five Years Pre-Research Project
Scientific Research Plan Projects of Shannxi Education Department
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/9633014/09810293.pdf?arnumber=9810293
Reference60 articles.
1. Urban Land Use and Land Cover Classification Using Remotely Sensed SAR Data through Deep Belief Networks
2. Towards adaptive learning with improved convergence of deep belief networks on graphics processing units
3. Semisupervised Deep Convolutional Neural Networks Using Pseudo Labels for PolSAR Image Classification
4. A Graph-Based Semisupervised Deep Learning Model for PolSAR Image Classification
5. Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification
Cited by 16 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. PolSAR Image Classification Based on Multi-Modal Contrastive Fully Convolutional Network;Remote Sensing;2024-01-11
2. PolSAR-MPIformer: A Vision Transformer Based on Mixed Patch Interaction for Dual-Frequency PolSAR Image Adaptive Fusion Classification;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024
3. UNet-Like Remote Sensing Change Detection: A review of current models and research directions;IEEE Geoscience and Remote Sensing Magazine;2024
4. A Lightweight Riemannian Covariance Matrix Convolutional Network for PolSAR Image Classification;IEEE Transactions on Geoscience and Remote Sensing;2024
5. Unsupervised Classification for Multilook Polarimetric SAR Images via Double Dirichlet Process Mixture Model;IEEE Transactions on Geoscience and Remote Sensing;2024
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3