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Genetic algorithm-based decision tree classifier for remote sensing mapping with SPOT-5 data in the HongShiMao watershed of the loess plateau, China

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Abstract

The loess plateau in China has faced severe soil erosion and runoff. Check-dams are effective measures for soil and water conservation; concomitantly check-dam planning and construction urgently require current land use maps. Remote sensing technique plays a key role in achieving up-to-date land use maps. However, limited by the impact of hilly and gully terrain in the loess plateau, commonly used classifier for remote sensing data cannot achieve satisfactory results. In this paper, HongShiMao watershed in the loess plateau was chosen as the study area. Decision tree classifier (DTC) based on a genetic algorithm (GA) was applied to the land use classification automatically. Compared with the results by iterative self-organizing data analysis technique (ISODATA), GA-based DTC had much better results. Its total accuracy was 83.2% with a Kappa coefficient 0.807. The results also showed that most part of the study area belonged to the barren land with sparse grass or crop cover that attributed to the soil erosion and runoff.

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Acknowledgments

This research is partially supported by the National Natural Science Foundation Project No. 40471103, the Key Project of National Natural Science Foundation No. 30590374, and the Opening Foundation Project No.2005406311 of State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, China. Special thanks to Dr. Jiaping Wu of Zhejiang University and Dr. Minghua Zhang of University of California, Davis, for the help in revising the English expression.

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Authors and Affiliations

  1. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications, Chinese Academy of Sciences, P. O. Box 9718, Beijing, 100101, China

    Mingxiang Huang, Jianghua Gong, Chunbo Liu & Lihui Zhang

  2. Institute of Agricultural Remote Sensing and Information System, College of Environmental and Resources Science, Zhejiang University, 268 Kaixuan Road, Hangzhou, 310029, China

    Zhou Shi

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  1. Mingxiang Huang
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  2. Jianghua Gong
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  3. Zhou Shi
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  4. Chunbo Liu
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Correspondence to Mingxiang Huang.

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Huang, M., Gong, J., Shi, Z. et al. Genetic algorithm-based decision tree classifier for remote sensing mapping with SPOT-5 data in the HongShiMao watershed of the loess plateau, China. Neural Comput & Applic 16, 513–517 (2007). https://doi.org/10.1007/s00521-007-0104-z

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