Multifrequency PolSAR Image Fusion Classification Based on Semantic Interactive Information and Topological Structure

Author:

Cao Yice1,Wu Yan1ORCID,Li Ming2ORCID,Zheng Mingjie3,Zhang Peng2ORCID,Wang Jili3

Affiliation:

1. Remote Sensing Image Processing and Fusion Group, School of Electronic Engineering, Xidian University, Xi’an, China

2. National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China

3. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

Funder

Natural Science Foundation of China

Civil Space Thirteen Five Years Pre-Research Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Reference40 articles.

1. Variational Learning of Mixture Wishart Model for PolSAR Image Classification

2. Graph sample and aggregate-attention network for hyperspectral image classification;ding;IEEE Geosci Remote Sens Lett,2021

3. Polarimetric SAR Image Classification Using Geodesic Distances and Composite Kernels

4. Inductive representation learning on large graphs;hamilton;Proc 31st Int Conf Neural Inf Process Syst,2017

5. POL-SAR Image Classification Based on Wishart DBN and Local Spatial Information

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3. 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

4. PolSAR Image Classification Via a Multigranularity Hybrid CNN-ViT Model With External Tokens and Cross-Attention;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

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