Skip to main content
SpringerLink
Log in

An application of tree species classification using high-resolution remote sensing image based on the rough set theory

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Feature extraction is an essential task in the classification of high-resolution remote sensing images, with the primary technique being the object-oriented classification method. Current research describes object-oriented classification methods by using remote sensing data, wherein how to reduce the redundant feature information to achieve good classification results is the most challenging problem. The high-resolution remote sensing image is characteristic of a large amount of data and high feature dimensions, which also exist particularly in the forestry remote sensing. Feature information redundancy can reduce the extraction accuracy and make the classification results worse. To address this problem, in this paper we propose a framework that uses the rough set theory and the membership function to establish the classification rule set. In our approach, we first select an optimal segmentation scale to segment the remote sensing image with multi-scale and apply the rough set theory to reduce the feature dimensions of objects. We then use the selected features to establish classification rule set and classify image objects. This paper also presents a detailed study of the proposed framework for species classification with ALOS images, wherein 13 most effective feature parameters are selected from 34 feature parameters of objects, such as band ratio, brightness value, and average gray value. Our experimental results demonstrate that the proposed framework, applied to classify tree species, achieves a classification accuracy of 80.4509%, which is an improvement over both the classification accuracy of 77.2408% achieved with the traditional supervised classification and that of 75.5068% achieved with the nearest neighbor classification. The research proves that the proposed framework can effectively take advantage of tree species information in remote sensing images, and provides an auxiliary means for forest resources investigation and monitoring.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Brauner N, Shacham M (2000) Considering precision of data in reduction of dimensionality and PCA. Comput Chem Eng 24(12):2603–2611

    Article  Google Scholar 

  2. Bruzzone L, Carlin L (2006) A multilevel context-based system for classification of very high spatial resolution images. IEEE Trans Geosc Remote Sens 44(9):2587–2600

    Article  Google Scholar 

  3. Cao X, Ke CQ (2006) Classification of high-resolution remote sensing images using object-oriented method. Remote Sens Info 58(5):27–28

    Google Scholar 

  4. Chen YH, Feng T, Shi PJ, et al. (2006) Classification of Remote Sensing Image Based on Object Oriented and Class Rules. Geomatics Inf Sci Wuhan Univ

  5. Chen ZQ, Shi JS, Bai FX (2013) Automatic image classification based on fuzzy-rough set. J Jilin Univ 43:209–212

    Google Scholar 

  6. Dash M, Liu H (1997) Feature selection for classification. Intelligent Data Anal 1(3):131–156

    Article  Google Scholar 

  7. Guo YG, Yu XF, Jiang D (2012) Study on forest classification based on object oriented technique. J Geogr Inf Sci 14(4):514–522

    Google Scholar 

  8. Hu QT, Yuan ZP, Zhou ZH (2008) Data extraction based on rough set and genetic algorithm. J Jiangxi Blue Sky Univ 11:336–340

    Google Scholar 

  9. Huang L (2011) Laplacian Eigenmaps for hyperspectral remote sensing image dimensionality reduction. Remote Sens Info 39(6):37–41

    Google Scholar 

  10. Huang QY (2012) RS image classification based on rough sets theory and SVM classification algorithm. Kunming Univ Sci Technol

  11. Law MHC, Jain AK (2006) Incremental nonlinear dimensionality reduction by manifold learning. IEEE Trans Pattern Anal Mac Intell 28(3):377–391

    Article  Google Scholar 

  12. Leung Y, Fung T, Mi JS et al (2007) A rough set approach to the discovery of classification rules in spatial data[J]. Int J Geogr Inf Sci 21(9):1033–1058

    Article  Google Scholar 

  13. Li HM, Zhou GH, Wang KQ (2007) A knowledge discovery method based on rough set theory and genetic algorithm. Comput Appl 8(1):76–78

    Google Scholar 

  14. Li M, Cui SY, Li CM et al (2008) Object oriented information extraction from high resolution remote sensing imagery—taking the extraction of farmland for an example. Remote Sens Info 18(6):63–65

    Google Scholar 

  15. Licciardi GA, Khan MM, Chanussot J et al (2011) Fusion of hyperspectral and panchromatic images using multiresolution analysis and nonlinear PCA band reduction. Eurasip J Adv Signal Process 2012(1):1–17

    Google Scholar 

  16. Lin C, Gong ZN, Zhao WJ (2010) The extraction of wetland hydrophytes types based on medium resolution TM data. Acta Ecol Sin 30(23):6460–6469

    Google Scholar 

  17. Liu P, Choo KKR, Wang L, et al. (2016) SVM or deep learning? A comparative study on remote sensing image classification. Soft Comput

  18. Ma Y, Wu H, Wang L et al (2015) Remote sensing big data computing: challenges and opportunities. Futur Gener Comput Syst 51:47–60

    Article  Google Scholar 

  19. Maaten LJPVD, Postma EO, Herik HJVD (2007) Dimensionality reduction: a comparative review. J Mach Learn Res 10(1)

  20. Mu HJ, Er X, Jing CM (2006) Discretization algorithm applied to interval numbers. Comput Eng Appl

  21. Myint SW, Lam NS-N, Tyler JM (2004) Wavelets for urban spatial feature discrimination: comparisons with fractal, spatial autocorrelation, and spatial Co-occurrence approaches. Photogramm Eng Remote Sens 70(7):803–812

    Article  Google Scholar 

  22. Nie Q, Ye XT (2012) Classification of high-resolution remote sensing image based on object oriented and feature combination. Urban Geotech Investig Surv

  23. Pawlak Z (1982) Rough sets. Int J Comput Inform Sci 11:314–356

    Article  MATH  Google Scholar 

  24. Puri AB, Bhattacharyya B (2015) Remote sensing classification information extraction based on rough set theory. J Jilin Univ 45(4):1246–1256

    Google Scholar 

  25. Susmaga R, Słowiński R, Greco S et al (2000) Generation of reducts and rules in multi-attribute and multi-criteria classification. Control Cybern 29(4):969–988

    MATH  Google Scholar 

  26. Tang TQ (2015) Research on vegetation information extraction using high-resolution remotely sensed image object-oriented method based. Jilin Univ

  27. Tian XG, Zhang JX, Zhang YH (2007) Extraction of mangrove information using object-oriented method. Hydrogr Surv Chart 27(2):41–44

    MathSciNet  Google Scholar 

  28. Wang QL (2008) Study on object-oriented remote sensing image classification and its application-taking urban vegetation extraction in Fujian, Shenzhen City for example, Nanjing Forestry University

  29. Wang L, Song W, Liu P (2016a) Link the remote sensing big data to the image features via wavelet transformation. Clust Comput 19(2):793–810

    Article  Google Scholar 

  30. Wang L, Ma Y, Yan J, et al. (2016b) pipsCloud: High performance cloud computing for remote sensing big data management and processing. Futur Gener Comput Syst

  31. Wang L, Zhang J, Liu P, et al. (2016c) Spectral–spatial multi-feature-based deep learning for hyperspectral remote sensing image classification. Soft Comput

  32. Xiao HG, Sang L, Ding SZ, Gong Y (2006) Rough set attribute reduction algorithm based on GA and its application. Comput Eng Appl

  33. Zhang ZC (2007) The wetland research with remote sensing image based on object-oriented classification in Xixi, Hangzhou. Zhejiang Univ

  34. Zhang Z, Wang J, Zha H (2012) Adaptive manifold learning. IEEE Trans Pattern Anal Mach Intell 34(2):253–265

    Article  Google Scholar 

  35. Zhu ZJ (2010) Study on mine area information extraction based on object-oriented high-resolution remote sensing image classification and its application, China University of Geosciences

Download references

Acknowledgments

This work was Supported by Beijing Natural Science Foundation (NO. 6164038) and China Fundamental Research Funds for the Central Universities (NO. TD2014-2). The authors would like to express their gratitude to the funds for the financial support.

Author information

Authors and Affiliations

  1. College of Information Science, Beijing Forestry University, Beijing, 100083, China

    Yi Zeng, Shuang Wang & Tianzhong Zhao

  2. State Key Laboratory of Resource and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing, China

    Jing Wang

Authors
  1. Yi Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tianzhong Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yi Zeng.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zeng, Y., Wang, S., Zhao, T. et al. An application of tree species classification using high-resolution remote sensing image based on the rough set theory. Multimed Tools Appl 76, 22999–23015 (2017). https://doi.org/10.1007/s11042-016-4210-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-4210-8

Keywords

Search

Navigation