双域融合网络提升极化雷达图像分类精度
DDF2Pol: A Dual-Domain Feature Fusion Network for PolSAR Image Classification
- 并行处理实值与复值特征，捕捉空间与极化信息
- 98.16%准确率（Flevoland）、96.12%（San Francisco）
- 参数仅9万，适合小数据场景
本文提出DDF2Pol，一种轻量级双域卷积神经网络，用于极化合成孔径雷达（PolSAR）图像分类。该架构包含两个并行的特征提取分支：一个处理实值数据，另一个处理复值数据，以捕获互补的空间与极化信息。为进一步优化特征，采用深度可分离卷积进行空间增强，并引入坐标注意力机制聚焦关键区域。在Flevoland和San Francisco两个基准数据集上的实验表明，DDF2Pol在保持低模型复杂度的同时实现了优异的分类性能：在Flevoland数据集上达到98.16%的总体准确率（OA），在San Francisco数据集上达96.12%，优于多种先进实值与复值模型。模型仅含91,371个参数，即使在训练数据有限的情况下仍具高效性。源代码已公开于https://github.com/mqalkhatib/DDF2Pol。
原文摘要 · Abstract (English)
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
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