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

Abstract

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 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 paper 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 data set for LCZ which covers 31 cities around the world. Results show that the proposed SAR4LCZ-Net improves 2.4% on overall accuracy and 4.5% on average accuracy compared to the real-valued CNN with same structure. Gaofen-3 quad-pol SAR data also showed its advantage over the dual-pol Sentinel-1 data. It enhanced 5.0% on overall accuracy and 7.2% on average accuracy in LCZ classification, under a fair comparison with a model trained by Sentinel-1 of the same area.