ZHAO Z Z, ZHOU Y Y, CHEN S Y. Classification methods for urban hierarchy and analysis of robust features using nighttime light remote sensing[J].Journal of Henan Polytechnic University(Natural Science) ,2025,44(6):156-164.

doi:10.16186/j.cnki.1673-9787.2023120025

Received: 2023/12/11

Revised: 2024/02/12

Published: 2025/10/14

Classification methods for urban hierarchy and analysis of robust features using nighttime light remote sensing

Zhao Zongze1, Zhou Yuyao1, Chen Shiyu2

1.School of Surveying and Land Information Engineering， Henan Polytechnic University， Jiaozuo  454000， Henan， China；2.School of Geographical Sciences， Xinyang Normal University， Xinyang  464000， Henan， China

Abstract: Objectives Urban hierarchy classification levels is closely related to urban planning and sustainable development. This study aims to evaluate the applicability of machine learning classification methods for urban hierarchy classification and analyze the robustness of nighttime light remote sensing features.  Methods Based on the 2021 NPP-VIIRS （National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer） nighttime light remote sensing imagery， 12 statistical features were extracted from 297 cities in China. Machine learning methods were employed for urban hierarchy recognition and classification， and the SHAP （Shapley Additive exPlaneations） method was used for the model interpretation and robust feature identification. Results The results show that all machine learning methods achieved AUC values above 0.90 under ROC （Receiver Operating Characteristic） curves， demonstrating excellent classification performance. Among them， random forest exhibited the best performance. with an accuracy of 0.75 and an F₁ value of 0.764 6. The recognition ability of machine learning methods varied across different city tiers： The classification accuracy for third-tier and fourth-tier cities was significantly lower than that of first-tier， second-tier， and fifth-tier cities. Classification accuracy for third- and fourth-tier cities was significantly lower than for first-， second-， and fifth-tier cities. The ensemble model combining XGBoost and random forest achieved an overall accuracy of 0.783 3， with an F₁ value of 0.805 1. While feature importance varied across different models， the robust nighttime light features remained consistent.  Conclusions This study demonstrates that machine learning classification methods based on nighttime light remote sensing imagery can effectively identify urban hierarchies. The identification of robust nighttime light feature variables reduces the complexity of urban hierarchy classification and improves classification efficiency. This approach provides a scientific basis for urban classification.

Key words: nighttime light remote sensing; machine learning; urban hierarchy classification; feature importance; classification accuracy