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Decision tree-based classification in coastal area integrating polarimetric SAR and optical data

Yuanyuan Chen (College of Civil Engineering, Nanjing Forestry University, Nanjing, China)
Xiufeng He (School of Earth Sciences and Engineering, Hohai University, Nanjing, China)
Jia Xu (School of Earth Sciences and Engineering, Hohai University, Nanjing, China)
Lin Guo (Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing, China) (Key Laboratory of Mechanism, Prevention and Mitigation of Land Subsidence, MOE, Capital Normal University, Beijing, China)
Yanyan Lu (Research Academy of Natural Resources and Environment Audit, Nanjing Audit University, Nanjing, China)
Rongchun Zhang (School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, China)

Data Technologies and Applications

ISSN: 2514-9288

Article publication date: 14 October 2021

Issue publication date: 22 June 2022

161

Abstract

Purpose

As one of the world's most productive ecosystems, ecological land plays an important role in regional and global environments. Utilizing advanced optical and synthetic aperture radar (SAR) data for land cover/land use research becomes increasingly popular. This research aims to investigate the complementarity of fully polarimetric SAR and optical imaging for ecological land classification in the eastern coastal area of China.

Design/methodology/approach

Four polarimetric decomposition methods, namely, H/Alpha, Yamaguchi3, VanZyl3 and Krogager, were applied to Advanced Land Observing Satellite (ALOS) SAR image for scattering parameter extraction. These parameters were merged with ALOS optical parameters for subsequent classification using the object-based quick, unbiased, efficient statistical tree decision tree method.

Findings

The experimental results indicate that an improved classification performance was obtained in the decision level when merging the two data sources. In fact, unlike classification using only optical images, the proposed approach allowed to distinguish ecological land with similar spectrum but different scattering. Moreover, unlike classification using only polarimetric information, the integration of polarimetric and optical data allows to accurately distinguish reed from artemisia and sand from salt field and therefore achieve a detailed classification of the coastal area characteristics.

Originality/value

This research proposed an integrated classification method for coastal ecological land with polarimetric SAR and optical data. The object-based and decision-level fusion enables effective ecological land classification in coastal area was verified.

Keywords

Acknowledgements

This research was supported by the Natural Science Foundation of Jiangsu Province (Grant No. BK20180779), Natural Science Foundation of China (41830110, 41901401), and the Youth Science and Technology Innovation Fund Project of Nanjing Forestry University (Grant No. CX2018015) from China. The ALOS PALSAR imagery was provided by the Japan Aerospace Exploration Agency.

Citation

Chen, Y., He, X., Xu, J., Guo, L., Lu, Y. and Zhang, R. (2022), "Decision tree-based classification in coastal area integrating polarimetric SAR and optical data", Data Technologies and Applications, Vol. 56 No. 3, pp. 342-357. https://doi.org/10.1108/DTA-08-2019-0149

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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