Abstract

Urban areas are important for city planning, security, traffic purposes, decision makers etc. Remotely sensed data are useful to detect urban areas either with active or passive systems. Each system has advantages and disadvantages. Passive images are mainly multispectral images and have rich information with their rich spectral resolution. In addition, they are affected by the atmospheric conditions, so there should not be clouds over the sensed region during data acquisition. On the other hand, SAR (Synthetic Aperture Radar) systems are not affected by the atmospheric conditions, but their spectral resolution is low, with mainly one-channel SAR systems. Also, the structure of passive images is completely different from that of multispectral images. Moreover, the geometrical and electrical properties of objects play an important role in the pixel values. In this study, a multispectral GOKTURK-2 MS (Multispectral) image and a SENTINEL 1A SAR image were used to detect urban buildings, using the advantages of both datasets. Firstly, the SVM (Support Vector Machines) method was applied to detect the buildings in the GOKTURK image. Then, the buildings were detected from the SAR image with the fuzzy logic approach. Finally, the buildings were detected by intersecting the results from both methods. The results from the SAR image could eliminate the false negative results from the GOKTURK-2 image. The study area was selected in Antalya province, Kepez district. The detected urban area was 288.353 m2 in the selected study area.

Highlights

  • The buildings are important objects for many purposes, such as city planning, flood simulation, real estate, municipality progress, etc

  • Aperture Radar) satellites can operate in all weather conditions, 24 h per day, since they use their own energy to detect the radiation reflected from the Earth surface

  • The support vector machine (SVM) classification method was applied on multispectral images, and fuzzy clustering was used for detecting the buildings from the SAR data

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Summary

Introduction

The buildings are important objects for many purposes, such as city planning, flood simulation, real estate, municipality progress, etc. Satellite data are efficient sources for detecting and updating buildings. Optical satellite image data were used for building detection purposes in the past [1–5]. Aperture Radar) satellites can operate in all weather conditions, 24 h per day, since they use their own energy to detect the radiation reflected from the Earth surface. This makes SAR remote sensing time- and weather-independent. The buildings were detected from multispectral Gokturk images and Sentinel 1A. The support vector machine (SVM) classification method was applied on multispectral images, and fuzzy clustering was used for detecting the buildings from the SAR data. The intersection of the results from the two datasets gave the most accurate detection results

Used Data
Method
Building Detection from the Gokturk Image
Building Detection from Sentinel Image
Results and Discussion
Conclusions
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