PolSAR Image Classification Using Hybrid Conditional Random Fields Model Based on Complex-Valued 3-D CNN

Author:

Zhang PengORCID,Tan XiaofengORCID,Li Beibei,Jiang Yinyin,Song WanyingORCID,Li MingORCID,Wu YanORCID

Funder

National Natural Science Foundation of China

Open-Ended Foundation of National Radar Signal Processing Laboratory

Civil Space 13th Five Years Pre-Research Project

Natural Science Basic Research Plan in Shaanxi Province of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Aerospace Engineering

Cited by 18 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. Symmetric Positive Definite Convolution Network for Polarimetric SAR Image Classification;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

2. Region Partition based Hybrid Deep Network for Polarimetric SAR Image Classification;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

3. SAR image classification using deep features and neighborhood heterogeneity confidence refined spatial constraints;International Journal of Remote Sensing;2024-02-14

4. Adversarial Network With Higher Order Potential Conditional Random Field for PolSAR Image Classification;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

5. A Lightweight Riemannian Covariance Matrix Convolutional Network for PolSAR Image Classification;IEEE Transactions on Geoscience and Remote Sensing;2024

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