PolSAR Image Classification Using Attention Based Shallow to Deep Convolutional Neural Network

This paper proposes a novel multi-branch feature fusion network for PolSAR image classification and interpretation. It is built using Complex-valued Convolutional Neural Networks (CV-CNNs). The proposed approach utilizes extraction of polarimetric features at each branch to achieve high classification accuracy. Moreover, Squeeze and Excitation (SE) is also introduced within the model’s architecture. SE block improves channel interdependencies with almost no additional computational cost. The proposed approach is tested and evaluated using Flevoland benchmark dataset. Experiments demonstrate the effectiveness of the proposed attention based shallow to deep CV-CNN model for PolSAR image classification in terms of Kappa Coefficient (k), Overall Accuracy (OA), and Average Accuracy (AA) metrics.