  Polarimetric synthetic aperture radar (PolSAR) images encompass valuable
information that can facilitate extensive land cover interpretation and
generate diverse output products. Extracting meaningful features from PolSAR
data poses challenges distinct from those encountered in optical imagery. Deep
learning (DL) methods offer effective solutions for overcoming these challenges
in PolSAR feature extraction. Convolutional neural networks (CNNs) play a
crucial role in capturing PolSAR image characteristics by leveraging kernel
capabilities to consider local information and the complex-valued nature of
PolSAR data. In this study, a novel three-branch fusion of complex-valued CNN,
named the Shallow to Deep Feature Fusion Network (SDF2Net), is proposed for
PolSAR image classification. To validate the performance of the proposed
method, classification results are compared against multiple state-of-the-art
approaches using the airborne synthetic aperture radar (AIRSAR) datasets of
Flevoland and San Francisco, as well as the ESAR Oberpfaffenhofen dataset. The
results indicate that the proposed approach demonstrates improvements in
overallaccuracy, with a 1.3% and 0.8% enhancement for the AIRSAR datasets and a
0.5% improvement for the ESAR dataset. Analyses conducted on the Flevoland data
underscore the effectiveness of the SDF2Net model, revealing a promising
overall accuracy of 96.01% even with only a 1% sampling ratio.
