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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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References

  1. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Chapman & Hall, New York

  2. Brumby SP, Theiler J, Bloch JJ, Harvey NR, Perkins S, Szymanski JJ, Young AC (2002) Evolving land cover classification algorithms for multi-spectral and multi-temporal imagery. Proc SPIE 4480, pp 120–129

  3. DeFries RS, Hansen MC, Townshend JRG, Sohlberg RS (1998) Global land cover classifications at 8 km spatial resolution: the use of training data derived from Landsat imagery in decision tree classifiers. Int J Remote Sens 19:3141–3168

    Article  Google Scholar 

  4. DeFries RS, Townshend JRG (1994) Global land cover: comparison of groundbased datasets to classifications with AVHRR data. In: Environmental remote sensing from regional to global scales. Wiley, Chichester

  5. De-Merckt TV (1993) Decision trees in numerical attribute spaces. In: Proceedings of the 13th international joint conference on artificial intelligence, pp 1016–1021

  6. Dimitris K, Christos P (2006) Analyzing student performance in distance learning with genetic algorithms and decision trees. Appl Artif Intell 20(8):655–674

    Article  Google Scholar 

  7. Friedl MA, Brodley CE (1997) Decision tree classification of land cover from remotely sensed data. Remote Sens Environ 61:399–409

    Article  Google Scholar 

  8. Gelfand SB, Ravishankar CS, Delp EJ (1991) An iterative growing and pruning algorithm for classification tree design. IEEE Trans Pattern Anal Mach Intell 13:163–174

    Article  Google Scholar 

  9. Giacinto G, Roli F (1997) Ensembles of neural networks for soft classification of remote sensing images. Proc of the European symposium on intelligent techniques Bari, Italy, pp 166–170

  10. Llorà X, Garrell JM (2001) Evolution of decision trees. Proceeding of the fourth catalan conference on artificial inteligence (CCIA’2001), ACIA Press

  11. Mitchell T (1997) Machine learning. McGraw Hill, New York

  12. Pal M, Mather PM (2003) An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sens Environ 86:554–565

    Article  Google Scholar 

  13. Palaniappan K, Zhu F, Zhuang X, Zhao Y, Blanchard A (2000) Enhanced binary tree genetic algorithm for automatic land cover classification. IEEE 2000 international geoscience and remote sensing symposium, pp 688–692

  14. Papagelis A, Kalles D (2001) Breeding decision trees using evolutionary techniques. Proceedings of the eighteenth international conference on machine learning, Morgan Kaufmann, San Francisco, June 28–July 01, pp 93–400

  15. Quinlan R (1986) Induction of decision trees. Mach Learn 1(1):81–106

    Google Scholar 

  16. Quinlan R (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Francisco

  17. Shi Z, Wang RC, Huang MX, Landgraf D (2002) Detection of coastal saline land uses with multi-temporal Landsat images in Shangyu city, China. Environ Manage 30(1):142–150

    Article  Google Scholar 

  18. Szymanski JP, Brumby SP, Pope P, Eads DR, Esch-Mosher D, Galassi M, Harvey N R, McCulloch HDW, Perkins SJ, Porter R, Theiler J, Young AC, Bloch JJ, David N (2002) Feature extraction from multiple data sources using genetic programming. Proc SPIE 4725, pp 338–345

  19. Townshend JRG, Justice CO, Li W, Gurney C, McManus J (1991) Global land cover classification by remote sensing: present capabilities and future possibilities. Remote Sens Environ 35:243–256

    Article  Google Scholar 

Download references

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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  5. Lihui Zhang
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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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  • DOI: https://doi.org/10.1007/s00521-007-0104-z

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