Geomatics and Environmental Engineering, vol. 16, no. 2

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Vol. 16 No. 2 (2022): Geomatics and Environmental Engineering

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Submitted
Jul 8, 2021
Accepted
Jan 13, 2022
Published
Feb 9, 2022
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This work is licensed under a Creative Commons Attribution 4.0 International License.

An Evaluation of Pixel-based and Object-based Classification Methods for Land Use Land Cover Analysis Using Geoinformatic Techniques

https://doi.org/10.7494/geom.2022.16.2.61
Sudhir K. Powar
Shivaji University, Department of Geography, Kolhapur, Maharashtra, India
https://orcid.org/0000-0003-4591-3084
Sachin S. Panhalkar
Shivaji University, Department of Geography, Kolhapur, Maharashtra, India
Abhijit S. Patil
Shivaji University, Department of Geography, Kolhapur, Maharashtra, India
https://orcid.org/0000-0003-1985-0921

Corresponding Author(s) : Sudhir K. Powar

sudhirpowar100@gmail.com

Geomatics and Environmental Engineering, Vol. 16 No. 2 (2022): Geomatics and Environmental Engineering

Abstract

Land use land cover (LULC) classification is a valuable asset for resource managers; in many fields of study, it has become essential to monitor LULC at different scales. As a result, the primary goal of this work is to compare and contrast the performance of pixel-based and object-based categorization algorithms. The supervised maximum likelihood classifier (MLC) technique was employed in pixel-based classification, while multi-resolution segmentation and the standard nearest neighbor (SNN) algorithm were employed in object-based classification. For the urban and suburban parts of Kolhapur, the Resourcesat-2 LISS-IV image was used, and the entire research region was classified into five LULC groups. The performance of the two approaches was examined by comparing the classification results. For accuracy evaluation, the ground truth data was used, and confusion matrixes were generated. The overall accuracy of the object-based methodology was 84.66%, which was significantly greater than the overall accuracy of the pixel-based categorization methodology, which was 72.66%. The findings of this study show that object-based classification is more appropriate for high-resolution Resourcesat-2 satellite data than MLC of pixel-based classification.

Keywords

pixel-based classification (PBC) object-based classification (OBC) maximum likelihood classifier multi-resolution segmentation
Powar, S. K., Panhalkar, S. S., & Patil, A. S. (2022). An Evaluation of Pixel-based and Object-based Classification Methods for Land Use Land Cover Analysis Using Geoinformatic Techniques. Geomatics and Environmental Engineering, 16(2), 61–75. https://doi.org/10.7494/geom.2022.16.2.61
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