PolSAR Image Classification Using a Hybrid Complex-Valued Network (HybridCVNet)
Mohammed Q. Alkhatib
Abstract
Recently, convolutional neural networks (CNNs) have become popular for image classification due to their effectiveness in computer vision tasks. Now, researchers are exploring the potential of vision transformers (ViTs) in remote sensing and Earth observation. However, traditional real-valued networks often overlook important phase information in complex-valued (CV) data such as polarimetric synthetic aperture radar (PolSAR) data. To address this, new CV deep architectures have emerged. HybridCVNet, a novel hybrid network, blends CV convolutional neural network (CV-CNN) and CV vision transformer (CV-ViT) techniques. It efficiently combines CV 3-D and 2-D CNNs as feature extractors, enhancing PolSAR image classification by extracting complementary information and effectively leveraging interdependencies within the data. Experimental results from widely used PolSAR datasets show HybridCVNet outperforms other methods, achieving an overall accuracy (OA) of 97.39% on the Flevoland dataset and showing promise even with just a 1% sampling ratio, with a Kappa value of 0.972 on the San Francisco dataset. The source code is accessible through <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/mqalkhatib/HybridCVNet</uri>.