Comparison of Pixel-Based and Object-Based Classification Methods in Evaluating Different High-Resolution Remote Sensing Data

Year 2025, Volume: 9 Issue: Special, 103 - 114, 28.12.2025

Mesut Çoşlu , Namık Kemal Sönmez

https://doi.org/10.31015/2025.si.18

Abstract

In recent years, with advancements in both computer and sensor technologies, new digital image processing techniques are frequently used in the processing of remote sensing data. In this context, object-based image analysis stands out, especially in the analysis of high spatial resolution data. This study aims to evaluate the pixel-based and object-based classification performances of high-resolution unmanned aerial vehicle (UAV) and WorldView-4 (WV4) satellite data and to determine the effect of vegetation indices added as additional bands to high-resolution data on the object-based classification result. According to the findings of the study, the highest overall accuracy (75.40%) was determined for the six-band UAV data. In the object-based classification phase of the study, it was determined that the vegetation indices added as additional bands to WV4, and UAV data increased the quality of the object-based classification process by an average of 2.43%. The findings obtained from the research indicated that adding additional bands to UAV data increased the overall accuracy in object-based classification.

Keywords

LULC , Pixel-Based Classification , Object-Based Image Analysis , UAV , WV4

Thanks

This study is derived from the first author's master’s thesis.

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