Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13087/1342
Title: Optimization of Remote Sensing Image Attributes to Improve Classification Accuracy
Authors: Matcı, Dilek Küçük
Avdan, Uğur
Keywords: Biyoloji Çeşitliliğinin Korunması
Biyoloji
Ekoloji
Çevre Bilimleri
Oşinografi
Su Kaynakları
Jeokimya ve Jeofizik
Jeoloji
Meteoroloji ve Atmosferik Bilimler
Arkeoloji
Issue Date: 2019
Abstract: Remote sensing technologies provide very important big data to various science areas such as risk identification, damage detection and prevention studies. However, the classification processes used to create thematic maps to interpret this data can be ineffective due to the wide range of properties that these images provide. At this point, there arises a requirement to optimize the data. The first objective of this study is to evaluate the performance of the Bat Search Algorithm which has not previously been used for improving the classification accuracy of remotely sensed images by optimizing attributes. The second objective is to compare the performance of the Genetic Algorithm, Bat Search Algorithm, Cuckoo Search Algorithm and Particle Swarm Optimization Algorithm, which are used in many areas of the literature for the optimization of the attributes of remotely sensed images. For these purposes, an image from the Landsat 8 satellite is used. The performance of the algorithms is compared by classifying the image using the K-Means method. The analysis shows a 10-22% increase in overall accuracy with the addition of attribute optimization.
URI: https://doi.org/10.30897/ijegeo.466985
https://hdl.handle.net/20.500.13087/1342
https://search.trdizin.gov.tr/yayin/detay/334560
ISSN: 2148-9173
Appears in Collections:TR-Dizin İndeksli Yayınlar Koleksiyonu
Uçak Gövde ve Motor Bakımı Bölümü Kolekiyonu

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