2026-10-18T23:15:05Zhttps://ebyu-rdlopen.rawdatalibrary.net/oai/request

oai:ebyu-rdlopen.rawdatalibrary.net:ebyu/149322026-10-18T20:36:52Zopenairedrivercom_ebyu_10000col_ebyu_10001

Improving classification accuracy of spectrally similar land covers in the rangeland and plateau areas with a combination of WorldView-2 and UAV imagesAkar, A.Gökalp, E.Akar, Ö.Yılmaz, V.object based classificationRangelandssupport vector machinesunmanned aerial vehicleWorldView-2accuracy assessmentimage classificationland coverplateaurangelandsupport vector machineunmanned vehicleWorldViewThis study aims to increase the accuracy of the object based classification approach to differentiate the spectrally similar land cover types to create thematic maps depicting the current land use status in rangeland. Firstly, the multispectral and panchromatic bands of a WorldView-2 MS and Pan images are fused. The fused WV-2 image is then classified with object based approach using Support Vector Machines (SVMs) classifier (Method 1). The overall classification accuracy for Method 1 is found to be 88.6%. Secondly, UAV ortho-image is utilised for segmentation process, which is required for the object based SVM classification of the WV-2 MS image (Method 2). The overall classification accuracy for Method 2 is obtained as 92.4%. It is realised that the Method 2 increases the object based classification accuracy by 4%, compared to Method 1. This result reveals that the object based classification of the UAV and WV-2 MS images makes significant contribution to the classification accuracy. © 2016 Informa UK Limited, trading as Taylor & Francis Group.Taylor and Francis Ltd.2017Articlehttps://rdlopen.rawdatalibrary.net/r/ebyu/14932https://doi.org/10.1080/10106049.2016.11788161010-6049enGeocarto Internationalinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/14932