Clustering-Based Class Hierarchy Modeling for Semantic Segmentation Using Remotely Sensed Imagery

Lanfa Liu, Song Wang, Zichen Tong and Zhanchuan Cai ()
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Lanfa Liu: State Key Laboratory of Lunar and Planetary Sciences, Macau University of Science and Technology, Macau 999078, China
Song Wang: Key Laboratory for Geographical Process Analysis and Simulation of Hubei Province, College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
Zichen Tong: Key Laboratory for Geographical Process Analysis and Simulation of Hubei Province, College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
Zhanchuan Cai: State Key Laboratory of Lunar and Planetary Sciences, Macau University of Science and Technology, Macau 999078, China

Mathematics, 2025, vol. 13, issue 3, 1-13

Abstract: Land use/land cover (LULC) nomenclature is commonly organized as a tree-like hierarchy, contributing to hierarchical LULC mapping. The hierarchical structure is typically defined by considering natural characteristics or human activities, which may not optimally align with the discriminative features and class relationships present in remotely sensed imagery. This paper explores a novel cluster-based class hierarchy modeling framework that generates data-driven hierarchical structures for LULC semantic segmentation. First, we perform spectral clustering on confusion matrices generated by a flat model, and then we introduce a hierarchical cluster validity index to obtain the optimal number of clusters to generate initial class hierarchies. We further employ ensemble clustering techniques to yield a refined final class hierarchy. Finally, we conduct comparative experiments on three benchmark datasets. Results demonstrating that the proposed method outperforms predefined hierarchies in both hierarchical LULC segmentation and classification.

Keywords: data-driven hierarchy; hierarchical segmentation; LULC; remote sensing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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