Multi-Feature Lightweight DeeplabV3+ Network for Polarimetric SAR Image Classification with Attention Mechanism

Polarimetric Synthetic Aperture Radar (PolSAR) is an advanced remote sensing technology that provides rich polarimetric information. Deep learning methods have been proved an effective tool for PolSAR image classification. However, relying solely on source data input makes it challenging to effectively classify all land cover targets, especially heterogeneous targets with significant scattering variations, such as urban areas and forests. Besides, multiple features can provide more complementary information, while feature selection is crucial for classification. To address these issues, we propose a novel attention mechanism-based multi-feature lightweight DeeplabV3+ network for PolSAR image classification. The proposed method integrates feature extraction, learning, selection, and classification into an end-to-end network framework. Initially, three kinds of complementary features are extracted to serve as inputs to the network, including polarimetric original data, statistical and scattering features, textural and contour features. Subsequently, a lightweight DeeplabV3+ network is designed to conduct multi-scale feature learning on the extracted multidimensional features. Finally, an attention mechanism-based feature selection module is integrated into the network model, adaptively learning weights for multi-scale features. This enhances discriminative features but suppresses redundant or confusing features. Experiments are conducted on five real PolSAR data sets, and experimental results demonstrate the proposed method can achieve more precise boundaries and smoother regions than the state-of-the-art algorithms. In this paper, we develop a novel multi-feature learning framework, achieving a fast and effective classification network for PolSAR images.