This paper investigates the application of Complex-Valued Convolutional Neural Networks (CV-CNNs) in polarimetric synthetic aperture radar (PolSAR) image classification. Unlike traditional real-valued networks that ignore the complex nature of radar signals, CV-CNNs operate directly in the complex domain, enabling the simultaneous preservation of both amplitude and phase information. By utilizing complex-valued backpropagation and activation functions, the proposed model captures the inherent physical scattering mechanisms of land cover more effectively. Experimental results on benchmark PolSAR datasets demonstrate that CV-CNNs achieve higher classification accuracy and better feature representation compared to their real-valued counterparts, offering a more robust solution for high-resolution remote sensing analysis.This paper investigates the application of Complex-Valued Convolutional Neural Networks (CV-CNNs) in polarimetric synthetic aperture radar (PolSAR) image classification. Unlike traditional real-valued networks that ignore the complex nature of radar signals, CV-CNNs operate directly in the complex domain, enabling the simultaneous preservation of both amplitude and phase information. By utilizing complex-valued backpropagation and activation functions, the proposed model captures the inherent physical scattering mechanisms of land cover more effectively. Experimental results on benchmark PolSAR datasets demonstrate that CV-CNNs achieve higher classification accuracy and better feature representation compared to their real-valued counterparts, offering a more robust solution for high-resolution remote sensing analysis.