A research on the effect of geostatistical texture analysis on image classification accuracy

AKYÜREK Ö., ARSLAN O.

GEOCARTO INTERNATIONAL, vol.37, no.27, pp.14925-14945, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 37 Issue: 27
  • Publication Date: 2022
  • Doi Number: 10.1080/10106049.2022.2092220
  • Journal Name: GEOCARTO INTERNATIONAL
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Environment Index, Geobase, INSPEC
  • Page Numbers: pp.14925-14945
  • Keywords: Texture, semivariogram, image classification, support vector machines, SEMIVARIOGRAM, INTEGRATION
  • Kocaeli University Affiliated: Yes

Abstract

In order to increase the accuracy results in various remote sensing applications, some additional parameters of texture information, which can provide information about the spatial relationship of a pixel in the image with other pixels, must be obtained. In addition to standard texture information extraction approaches, there is a need for approaches that take into account the spatial relationships of pixels in a certain neighborhood. In this study, it is aimed to reveal to what extent the use of characteristic information that can determine the texture properties of the image will affect the performance of the classification of satellite images by using the spatial dependency information obtained by geostatistical methods in satellite images. The data sets were classified with the Support Vector Machines (SVM) method, and according to pixel-based classification, an increase of 10% in agricultural area data and a 3% increase in urban area image was observed.