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An Automated Approach for Land Cover Classification Based on a Fuzzy Supervised Learning Framework

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Abstract

This paper proposes an automatic framework for land cover classification. In majority of published work by various researchers so far, most of the methods need manually mark the label of land cover types. In the proposed framework, all the information, like land cover types and their features, is defined as prior knowledge achieved from land use maps, topographic data, texture data, vegetation’s growth cycle and field data. The land cover classification is treated as an automatically supervised learning procedure, which can be divided into automatic sample selection and fuzzy supervised classification. Once a series of features were extracted from multi-source datasets, spectral matching method is used to determine the degrees of membership of auto-selected pixels, which indicates the probability of the pixel to be distinguished as a specific land cover type. In order to make full use of this probability, a fuzzy support vector machine (SVM) classification method is used to handle samples with membership degrees. This method is applied to Landsat Thematic Mapper (TM) data of two areas located in Northern China. The automatic classification results are compared with visual interpretation. Experimental results show that the proposed method classifies the remote sensing data with a competitive and stable accuracy, and demonstrate that an objective land cover classification result is achievable by combining several advanced machine learning methods.

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References

  • Aitkenhead, M. J., & Aalders, I. H. (2011). Automating land cover mapping of Scotland using expert system and knowledge integration methods. Remote Sensing of Environment, 115(5), 1285–1295.

    Article  Google Scholar 

  • Belousov, A. I., Verzakov, S. A., & von Frese, J. (2002). A flexible classification approach with optimal generalisation performance: support vector machines. Chemometrics and Intelligent Laboratory Systems, 64(1), 15–25.

    Article  Google Scholar 

  • Bengio, Y. (2009). Learning deep architectures for AI. Foundations and Trends in Machine Learning, 2(1), 1–127.

    Article  Google Scholar 

  • Berberoglu, S., Lloyd, C. D., Atkinson, P. M., & Curran, P. J. (2000). The integration of spectral and textural information using neural networks for land cover mapping in the Mediterranean. Computers & Geosciences, 26(4), 385–396.

    Article  Google Scholar 

  • Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2–16.

    Article  Google Scholar 

  • Chang, C. C., & Lin, C. J. (2011). LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 27.

    Google Scholar 

  • Chen, J., Wang, C., & Wang, R. S. (2009). Using stacked generalization to combine SVMs in magnitude and shape feature spaces for classification of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 47(7), 2193–2205.

    Article  Google Scholar 

  • Fardanesh, M. T., & Ersoy, O. K. (1998). Classification accuracy improvement of neural network classifiers by using unlabeled data. IEEE Transactions on Geoscience and Remote Sensing, 36(3), 1020–1025.

    Article  Google Scholar 

  • Foody, G. M., & Mathur, A. (2006). The use of small training sets containing mixed pixels for accurate hard image classification: training on mixed spectral responses for classification by a SVM. Remote Sensing of Environment, 103(2), 179–189.

    Article  Google Scholar 

  • Foody, G. M., Mathur, A., Sanchez-Hernandez, C., & Boyd, D. S. (2006). Training set size requirements for the classification of a specific class. Remote Sensing of Environment, 104(1), 1–14.

    Article  Google Scholar 

  • Franklin, S. E., & Wulder, M. A. (2002). Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas. Progress in Physical Geography, 26(2), 173–205.

    Article  Google Scholar 

  • Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., & Huang, X. (2010). MODIS collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114(1), 168–182.

    Article  Google Scholar 

  • Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507.

    Article  Google Scholar 

  • Homer, C. C., Huang, L., Yang, B. W., & Coan, M. (2004). “Development of a 2001 National Landcover Database for the United States.”. Photogrammetric Engineering and Remote Sensing, 70(7), 829–840.

    Article  Google Scholar 

  • Horn, B. (1981). Hill shading and the reflectance map. Proceedings of the IEEE, 69(1), 14–47.

    Article  Google Scholar 

  • Huang, C., Davis, L. S., & Townshend, J. (2002). An assessment of support vector machines for land cover classification. International Journal of Remote Sensing, 23(4), 725–749.

    Article  Google Scholar 

  • Inglada, J. (2007). Automatic recognition of man-made objects in high resolution optical remote sensing images by SVM classification of geometric image features. ISPRS Journal of Photogrammetry and Remote Sensing, 62(3), 236–248.

    Article  Google Scholar 

  • Keuchel, J., Naumann, S., Heiler, M., & Siegmund, A. (2003). Automatic land cover analysis for Tenerife by supervised classification using remotely sensed data. Remote Sensing of Environment, 86(4), 530–541.

    Article  Google Scholar 

  • Li, J., & Narayanan, R. M. (2004). Integrated spectral and spatial information mining in remote sensing imagery. IEEE Transactions On Geoscience and Remote Sensing, 42(3), 673–685.

    Article  Google Scholar 

  • Lin, C., & Wang, S. (2002). Fuzzy support vector machines. IEEE Transactions on Neural Networks, 13(2), 464–471.

    Article  Google Scholar 

  • Liu, Q. S., & Metaxas, D. N. (2009). Unifying subspace and distance metric learning with Bhattacharyya coefficient for image classification. Emerging Trends in Visual Computing, 5416, 254–267.

    Article  Google Scholar 

  • Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L., & Merchant, J. W. (2000). Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. International Journal of Remote Sensing, 21(6–7), 1303–1330.

    Article  Google Scholar 

  • Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823–870.

    Article  Google Scholar 

  • Lunetta, R. S., Knight, J. F., Ediriwickrema, J., Lyon, J. G., & Worthy, L. D. (2006). Land-cover change detection using multi-temporal MODIS NDVI data. Remote Sensing of Environment, 105(2), 142–154.

    Article  Google Scholar 

  • Maulik, U., & Chakraborty, D. (2011). A self-trained ensemble with semisupervised SVM: an application to pixel classification of remote sensing imagery. Pattern Recognition, 44(3), 615–623.

    Article  Google Scholar 

  • Melgani, F., & Bruzzone, L. (2004). Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42(8), 1778–1790.

    Article  Google Scholar 

  • Miller, J., & Franklin, J. (2002). Modeling the distribution of four vegetation alliances using generalized linear models and classification trees with spatial dependence. Ecological Modelling, 157(2), 227–247.

    Article  Google Scholar 

  • Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: a review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247–259.

    Article  Google Scholar 

  • Raun, W. R., Solie, J. B., Martin, K. L., Freeman, K. W., Stone, M. L., Johnson, G. V., & Mullen, R. W. (2005). Growth stage, development, and spatial variability in corn evaluated using optical sensor readings. Journal of Plant Nutrition, 28(1), 173–182.

    Article  Google Scholar 

  • Stehman, S. V. S. S. (2009). Sampling designs for accuracy assessment of land cover. International Journal of Remote Sensing, 30(20), 5243–5272.

    Article  Google Scholar 

  • Trias-Sanz, R., Stamon, G., & Louchet, J. (2008). Using colour, texture, and hierarchial segmentation for high-resolution remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 63(2), 156–168.

    Article  Google Scholar 

  • Tsai, F., & Philpot, W. D. (2002). A derivative-aided hyperspectral image analysis system for land-cover classification. IEEE Transactions on Geoscience and Remote Sensing, 40(2), 416–425.

    Article  Google Scholar 

  • Tuia, D., Ratle, F., Pacifici, F., Kanevski, M. F., & Emery, W. J. (2009). Active learning methods for remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 47(7), 2218–2232.

    Article  Google Scholar 

  • van der Meer, F. (2006). The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery. International Journal of Applied Earth Observation and Geoinformation, 8(1), 3–17.

    Article  Google Scholar 

  • Vapnik, V., & Vashist, A. (2009). A new learning paradigm: Learning using privileged information. Neural Networks, 22(5–6), 544–557.

    Article  Google Scholar 

  • Wardlow, B. D., & Egbert, S. L. (2003). A state-level comparative 7analysis of the GAP and NLCD land-cover data sets. Photogrammetric Engineering and Remote Sensing, 69(12), 1387–1397.

    Google Scholar 

  • Xing, E.P., Ng, A.Y., Jordan, M.I., & Russell, S. (2003). “Distance metric learning with application to clustering with side-information.” Advances in neural information processing systems 521-528.

  • Zhang, L. P., Huang, X., Huang, B., & Li, P. X. (2006). A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery. IEEE Transactions on Geoscience and Remote Sensing, 44(10Part 2), 2950–2961.

    Article  Google Scholar 

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Acknowledgments

This paper is supported by the National Natural Science Foundation of China with Grant NO. 41101398, 41271367 and 61340058; Key Projects in the National Science & Technology Pillar Program with Grant NO. 2011BAH06B02.

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Authors and Affiliations

  1. Institute of Remote Sensing and Digital Earth, University of Chinese Academy of Science, Beijing, 100101, China

    Liegang Xia, Jiancheng Luo & Zhanfeng Shen

  2. College of Software, Zhejiang University of Technology, Hangzhou, 310023, China

    Liegang Xia & Weihong Wang

Authors
  1. Liegang Xia
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  2. Jiancheng Luo
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  3. Weihong Wang
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  4. Zhanfeng Shen
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Correspondence to Liegang Xia.

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Xia, L., Luo, J., Wang, W. et al. An Automated Approach for Land Cover Classification Based on a Fuzzy Supervised Learning Framework. J Indian Soc Remote Sens 42, 505–515 (2014). https://doi.org/10.1007/s12524-013-0352-6

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  • DOI: https://doi.org/10.1007/s12524-013-0352-6

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