SAR4LCZ-Net: A Complex-Valued Convolutional Neural Network for Local Climate Zones Classification Using Gaofen-3 Quad-Pol SAR Data

The recent local climate zones (LCZ) classification scheme provides spatially fine granular descriptions of inner urban morphology. It is universally applicable to cities worldwide and capable of supporting various urban studies. Although optical and dual-pol synthetic aperture radar (SAR) data continue to push the frontiers of this task, the potential of quad-pol SAR data for LCZ classification is not yet explored. In this article, we propose a novel complex-valued convolutional neural network (CNN), SAR4LCZ-Net, to tackle this challenge. SAR4LCZ-Net improves the state-of-the-art by exploiting two facts of this specific task: the semantic hierarchical structure of the LCZ classification scheme and the complex-valued nature of quad-pol SAR data. To validate the performance of our algorithm, we generate a Chinese Gaofen-3 quad-pol SAR dataset for LCZ which covers 31 cities around the world. Results show that the proposed SAR4LCZ-Net improves 2.4% on overall accuracy (OA) and 4.5% on average accuracy (AA) compared with the real-valued CNN with the same structure. Gaofen-3 quad-pol SAR data also showed its advantage over the dual-pol Sentinel-1 data. It enhanced 5.0% on OA and 7.2% on AA in LCZ classification, under a fair comparison with a model trained by Sentinel-1 of the same area.