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

• Autorzy: Powar Sudhir K. , Panhalkar Sachin S. , Patil Abhijit S.

Identyfikatory

DOI

10.7494/geom.2022.16.2.61

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

pixel-based classification (PBC); object-based classification(OBC); maximum likelihood classifier; multi-resolution segmentation

• Wydawca: AGH University of Science and Technology Press
• Czasopismo: Geomatics and Environmental Engineering
• Rocznik: 2022
• Tom: Vol. 16, no. 2
• Strony: 61--75
• Opis fizyczny: Bibliogr. 27 poz., rys., tab., wykr.

Twórcy

autor

Powar Sudhir K.

• Shivaji University, Department of Geography, Kolhapur, Maharashtra, India

• https://orcid.org/0000-0003-4591-3084

autor

Panhalkar Sachin S.

• Shivaji University, Department of Geography, Kolhapur, Maharashtra, India

autor

Patil Abhijit S.

• Shivaji University, Department of Geography, Kolhapur, Maharashtra, India

• https://orcid.org/0000-0003-1985-0921

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu „Społeczna odpowiedzialność nauki” - moduł: Popularyzacja nauki i promocja sportu (2022-2023)