An Investigation on Land Cover Mapping Capability of Classical and Fuzzy based Maximum Likelihood Classifiers

  • Authors

    2018-06-06
    https://doi.org/10.14419/ijet.v7i2.10743
  • Accuracy assessment, Fuzzy topology, Mahalanobis distance, Maximum likelihood classification, Remote sensing.
  • Abstract

    In the past two decades, a significant amount of research has been conducted in the area of information extraction from heterogeneous remotely sensed (RS) datasets. However, it is arduous to exactly predict the behaviour of the classification technique employed due to issues such as the type of the dataset, resolution of the imagery, the presence of mixed pixels, and spectrally overlapping of classes. In this paper, land cover classification of the heterogeneous dataset using classical and Fuzzy based Maximum Likelihood Classifiers (MLC) is presented and compared. Three decision parameters and their significance in pixel assignment is illustrated. The presented Fuzzy based MLC uses a weighted inverse distance measure for defuzzification process. 10 pixels were randomly selected from the study area to illustrate pixel assignment for both the classifiers. The study aims at enhancing the classification accuracy of heterogeneous multispectral remote sensor data characterized by spectrally overlapping classes and mixed pixels. The study additionally aims at obtaining classification results with a confidence level of 95% with ±4% error margin. Classification success rate was analysed using accuracy assessment. Fuzzy based MLC produced significantly higher classification accuracy as compared to classical MLC. The conducted research achieves the expected classification accuracy and proves to be a valuable technique for classification of heterogeneous RS multispectral imagery. 

  • References

    1. [1] J.R. Jensen, Introductory digital image processing: a remote sensing perspective, Prentice-Hall Inc., (2000).

      [2] J.A. Richards, Remote Sensing Digital Image Analysis- An Introduction, Springer, (2006).

      [3] D. Lu, Q. Weng, “A survey of image classification methods and techniques for improving classification performanceâ€, International Journal of Remote Sensing, Vol.28, No.5, (2007), pp.823-870.

      [4] L. Bruzzone, D.F. Prieto, “Unsupervised Retraining of a Maximum-Likelihood Classifier for the Analysis of Multitemporal Remote-Sensing Imagesâ€, Proceedings of the EUROPTO Conference on Image and Signal Processing for Remote Sensing, 3871, (1999), pp.169-174.

      [5] D. Peuquet, D. Marble, D. Francis, Introductory Readings in Geographic Information Systems and Remote Sensing, CRC Press, (2003).

      [6] Y. Wang, M. Jamshidi, “Fuzzy logic applied in remote sensing image classificationâ€, Systems, Man and Cybernetics, Vol.7, (2004), pp.6378-6382.

      [7] F.Wang, “Fuzzy Supervised Classification of Remote Sensing Imagesâ€, IEEE Transactions on Geoscience and Remote Sensing, Vol.28, No.2, (1990), pp.194-201.

      [8] J. Zhang, G.M. Foody, “A fuzzy classification of sub-urban land cover from remotely sensed imagery.â€, International Journal of Remote Sensing, Vol.19, No.14, (1998), pp.2721-2738.

      [9] F. Melgani, B.A.R. Al Hashemy, S.M.R. Taha, “An explicit fuzzy supervised classification method for multispectral remote sensing images.â€, IEEE Transactions on Geoscience and Remote Sensing, Vol.38, No.1, (2000), pp.287-295.

      [10] K. Liu, W. Shi, H. Zhang, “A fuzzy theory-based maximum likelihood classification.â€, ISPRS Journal of Photogrammetry and Remote Sensing, Vol.66, No.1, (2011), pp.103-114.

      [11] E. Console, M.C. Mouchot, “Fuzzy Classification Techniques in the Urban Area Recognition.â€, Proceedings of the International Geoscience and Remote Sensing Symposium, (1996), pp.1373-1375.

      [12] G. Droj, “The applicability of fuzzy theory in remote sensing image classificationâ€, Studia Univ. Babes, Bolyai, Informatica, Vol.LII, No.1, (2007), pp.89-96.

      [13] F. Wang, “Improving Remote-Sensing Image-Analysis through Fuzzy Information Representationâ€, Photogrammetric Engineering and Remote Sensing, Vol.56, No.8, (1990), pp.1163-1169.

      [14] K. Zanter, Landsat 8 (L8) Data Users Handbook, LSDS-1574 Version. 2.0, Vol.2, (2016).

      [15] Directorate of Census Operations Karnataka, District census handbook Uttara Kannada, Census of India, (2014).

      [16] J.P. Pascal, Explanatory Booklet on Forest Map of South India: Belgaum-Dharwar-Panaji , Shimoga, Mercara-Mysore, Institut Francais de Pondichery: Travaux de la Section Scientifique et Technique. Hors Serie N 18, (1986).

      [17] USGS, Earthexplorer, https://earthexplorer.usgs.gov/.

      [18] P.H. Swain, S.M. Davis, Remote Sensing: The Quantitative Approach, McGraw-Hill, pp.166-174, (1978).

      [19] J. A. Richards, X. Jia, Remote Sensing Digital Image Analysis- An Introduction, Springer, (2006).

      [20] L. A. Zadeh, “Fuzzy setsâ€, Information and Control, Vol.8, No.3, (1965), pp.338-353.

      [21] R. Pouncey, K. Swanson, K. Hart, ERDAS Field Guide, ERDAS Inc., (1999).

      [22] R.G. Congalton, “A review of assessing the accuracy of classifications of remotely sensed dataâ€, Remote Sensing of Environment, Vol.37, No.1, (1991), pp.35-46.

  • Downloads

  • How to Cite

    B R, S., & S V, R. (2018). An Investigation on Land Cover Mapping Capability of Classical and Fuzzy based Maximum Likelihood Classifiers. International Journal of Engineering & Technology, 7(2), 939-947. https://doi.org/10.14419/ijet.v7i2.10743

    Received date: 2018-03-28

    Accepted date: 2018-05-21

    Published date: 2018-06-06