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

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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.

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Authors and Affiliations

  1. Department of Statistics and Operations Research, Aligarh Muslim University, Aligarh, 202 002, India

    A. R. Sherwani & Q. M. Ali

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  1. A. R. Sherwani
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  2. Q. M. Ali
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The authors contributed equally in the manuscript. All authors read and approved the final manuscript.

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Correspondence to A. R. Sherwani.

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Sherwani, A.R., Ali, Q.M. Parametric Classification using Fuzzy Approach for Handling the Problem of Mixed Pixels in Ground Truth Data for a Satellite Image. Ann. Data. Sci. 10, 1459–1472 (2023). https://doi.org/10.1007/s40745-022-00383-y

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  • DOI: https://doi.org/10.1007/s40745-022-00383-y

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