Author:
Tonbul Hasan,Kavzoglu Taskin
Abstract
Due to the complex spectral and spatial structures of remotely sensed images, the delineation of land use/land cover classes using conventional approaches is a challenging task. This article tackles the problem of seeking optimal parameters of multi-resolution segmentation for a classification
task using WorldView-2 imagery. Taguchi optimization was applied to search optimal parameters using the plateau objective function (<small>POF</small>) and quality rate (<small>Qr</small>) as fitness criteria. Analysis of variance was also used to estimate the contributions
of the parameters for POF and Qr, separately. The scale parameter was the most effective one, with contribution levels of 87.45% and 56.87% for POF and Qr, respectively. Linear regression and support-vector regression methods were used to predict the results of the experiment. Test results
revealed that Taguchi optimization was more effective than linear regression and support-vector regression for predicting POF and Qr values.
Publisher
American Society for Photogrammetry and Remote Sensing
Subject
Computers in Earth Sciences
Cited by
6 articles.
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