This research work presents a novel dual-branch model for hyperspectral image classification that combines two streams: one for processing standard hyperspectral patches using Real-Valued Neural Network (RVNN) and the other for processing their corresponding Fourier transforms using Complex-Valued Neural Network (CVNN). The proposed model is evaluated on the Pavia University and Salinas datasets. Results show that the proposed model outperforms state-of-the-art methods in terms of overall accuracy, average accuracy, and Kappa. Through the incorporation of Fourier transforms in the second stream, the model is able to extract frequency information, which complements the spatial information extracted by the first stream. The combination of these two streams improves the overall performance of the model. Furthermore, to enhance the model performance, the Squeeze and Excitation (SE) mechanism has been utilized. Experimental evidence show that SE block improves the models overall accuracy by almost 1\%.

翻译：本研究提出一种新型的双支路模型用于高光谱图像分类，该模型结合两条处理流：一条使用实值神经网络处理标准高光谱图像块，另一条使用复值神经网络处理对应的傅里叶变换。所提模型在帕维亚大学和萨利纳斯数据集上进行评估。结果表明，该模型在总体精度、平均精度和Kappa系数方面均优于现有最优方法。通过在第二条处理流中引入傅里叶变换，模型能够提取频域信息，从而补充第一条处理流提取的空间信息。两条处理流的结合提升了模型的整体性能。此外，为增强模型性能，采用了压缩激励机制。实验证据表明，压缩激励模块使模型的总体精度提升约1%。