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Parametric Classification using Fuzzy Approach for Handling the Problem of Mixed Pixels in Ground Truth Data for a Satellite Image

Author

Listed:
  • A. R. Sherwani

    (Aligarh Muslim University)

  • Q. M. Ali

    (Aligarh Muslim University)

Abstract

This study identified some of the problems of collecting ground truth data for supervised classification and the presence of mixed pixels. The mixed pixel problem is one of the main factors affecting classification precision in classifying the remotely sensed images. Mixed pixels are usually the biggest reason for degrading the success in image classification and object recognition.In this study, a fuzzy supervised classification method in which geographical information is represented as fuzzy sets is used to overcome the problem of mixed pixels. Partial membership of the mixed pixels allows component cover classes to be identified and more accurate statistical parameters to be generated. As a result, the error rates get reduced compared with the conventional classification methods like linear discriminant function(LDF) and quadratic discriminant function(QDF).The study used real satellite image data of some terrain in western Uttar Pradesh India.

Suggested Citation

  • A. R. Sherwani & Q. M. Ali, 2023. "Parametric Classification using Fuzzy Approach for Handling the Problem of Mixed Pixels in Ground Truth Data for a Satellite Image," Annals of Data Science, Springer, vol. 10(6), pages 1459-1472, December.
    • Handle: RePEc:spr:aodasc:v:10:y:2023:i:6:d:10.1007_s40745-022-00383-y
    DOI: 10.1007/s40745-022-00383-y
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    References listed on IDEAS

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    1. Wasim Akram Mandal, 2021. "Weighted Tchebycheff Optimization Technique Under Uncertainty," Annals of Data Science, Springer, vol. 8(4), pages 709-731, December.
    2. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
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