DDF2Pol: A Dual-Domain Feature Fusion Network for PolSAR Image Classification

2026-04-20

AI Summary

This study addresses the challenge of effectively integrating spatial and polarimetric information in polarimetric synthetic aperture radar (PolSAR) image classification by proposing a lightweight dual-domain convolutional network. The method introduces, for the first time, parallel real-valued and complex-valued convolutional streams to simultaneously capture complementary spatial structural and polarimetric scattering characteristics. Enhanced by depthwise convolutions and coordinate attention mechanisms, the model achieves superior feature representation with only 91,371 parameters. It attains overall classification accuracies of 98.16% and 96.12% on the Flevoland and San Francisco datasets, respectively, significantly outperforming current state-of-the-art approaches. These results demonstrate that the proposed dual-domain fusion strategy effectively boosts classification performance while maintaining a compact model architecture.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

This paper presents DDF2Pol, a lightweight dual-domain convolutional neural network for PolSAR image classification. The proposed architecture integrates two parallel feature extraction streams, one real-valued and one complex-valued, designed to capture complementary spatial and polarimetric information from PolSAR data. To further refine the extracted features, a depth-wise convolution layer is employed for spatial enhancement, followed by a coordinate attention mechanism to focus on the most informative regions. Experimental evaluations conducted on two benchmark datasets, Flevoland and San Francisco, demonstrate that DDF2Pol achieves superior classification performance while maintaining low model complexity. Specifically, it attains an Overall Accuracy (OA) of 98.16% on the Flevoland dataset and 96.12% on the San Francisco dataset, outperforming several state-of-the-art real- and complex-valued models. With only 91,371 parameters, DDF2Pol offers a practical and efficient solution for accurate PolSAR image analysis, even when training data is limited. The source code is publicly available at https://github.com/mqalkhatib/DDF2Pol

Problem

• PolSAR image classification
• dual-domain feature fusion
• lightweight network
• polarimetric information
• spatial enhancement

Innovation

• dual-domain feature fusion
• complex-valued CNN
• coordinate attention
• lightweight PolSAR classification
• depth-wise convolution