  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\%.
