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

Sudhir K Powar,Abhijit S Patil,Sachin S Panhalkar

doi:10.7494/geom.2022.16.2.61

Sudhir K Powar, Abhijit S Patil + Show 1 more

Open Access

https://doi.org/10.7494/geom.2022.16.2.61

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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.

Highlights

Land use and land cover (LULC) analysis is significant for land resource policy, land management, and land system analysis [1]
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
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

Summary

Introduction

Land use and land cover (LULC) analysis is significant for land resource policy, land management, and land system analysis [1]. The LULC data provides a panoramic view of the landscape’s characteristics and facilitates decision-making on interconnected parts of most land-based processes [3]. It can be obtained from satellite images using remote sensing image classification [4, 5]. Most LULC classifications have traditionally been based on the pixel-based classification (PBC) of remotely sensed images. They utilized supervised or unsupervised classification, or a combination of both [6]. Maximum likelihood classification (MLC) is one of the classification techniques of pixel-based image classification. It was found that when pixel-based methods were applied to high-resolution images, a “salt and pepper” noise was created, which added to the inaccuracy of the classification [9,10,11]

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