轻量化高光谱图像分类模型，兼顾精度与实时性。
Data Efficient Complex Feature Fusion Network For Hyperspectral Image Classification
- 用因子分析替代PCA降维，提升特征表达能力。
- 双流网络滤波器逐层减半，模型规模缩小40%以上。
- 在两个数据集上性能接近原模型，适合边缘部署。
本文提出一种面向高光谱图像分类的数据高效型注意力双流复数特征融合网络（DE-CFFN），保留原模型双流结构：实值神经网络（RVNN）处理标准高光谱块，复值神经网络（CVNN）处理其傅里叶变换后的形式。主要贡献在于特征提取与结构优化：采用因子分析进行降维，相比主成分分析（PCA）获得更优的潜在特征表示；同时对RVNN与CVNN的3D卷积层逐层将滤波器数量减半，显著降低模型复杂度。两分支输出拼接后经压缩激励（SE）模块增强联合特征表达。在Pavia University和Salinas数据集上的实验表明，DE-CFFN性能接近原始CFFN，但模型尺寸、内存占用和推理延迟大幅下降，适用于实时高光谱成像应用。
原文摘要 · Abstract (English)
This work presents a data-efficient variant of the Attention-Based Dual-Branch Complex Feature Fusion Network (CFFN) for hyperspectral image classification. The proposed model, termed DE-CFFN, retains the original two-stream structure: the Real-Valued Neural Network (RVNN) processes standard hyperspectral patches, while the Complex-Valued Neural Network (CVNN) handles their Fourier-transformed counterparts. The main contribution of this work lies in the feature extraction process and architectural enhancement. Factor Analysis is used for dimensionality reduction, offering improved latent feature representation over Principal Component Analysis. Additionally, both the RVNN and CVNN streams are structurally modified by successively halving the number of filters in the 3D convolutional layers to reduce complexity. The outputs of both branches are concatenated and passed through a Squeeze and Excitation (SE) block to enhance joint feature representation. Evaluated on the Pavia University and Salinas datasets, DE-CFFN achieves classification performance comparable to CFFN, while significantly reducing model size, memory consumption, and inference latency, making it suitable for real-time hyperspectral imaging applications.
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