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Kaynaştırılmış Görüntülerden Elde Edilen Gabor Doku Özellikleri ile DVM Sınıflandırma Performansının İyileştirilmesi

Year 2020, Volume: 1 Issue: 1, 34 - 44, 31.03.2020

Abstract

Görüntü kaynaştırma, uzaktan algılanan verilerin yorumlanabilirliğini ve işlevselliğini artırmak için en yaygın olarak kullanılan tekniklerden biridir. Bu çalışmanın amacı Destek Vektör Makineleri (DVM) sınıflandırma algoritmasının performansının kaynaştırılmış görüntülerden elde edilen doku özellikleri yardımıyla iyileştirilmesidir. Bu amaçla, ilk aşama olarak bir WorldView-2 çok bantlı görüntüsü bir WorldView-2 pankromatik görüntüsü ile PCA (Principal Component Analysis), WSB (Wavelet Single Band), GS (Gram-Schmidt), BRV (Brovey), EHL (Ehlers), HCS (Hyperspherical Colour Space), HPF (High-Pass Filtering) ve MCV (Multiplicative) yöntemleri kullanılarak kaynaştırılmıştır. Daha sonra her bir kaynaştırılmış görüntüye Temel Bileşenler Analizi uygulanmıştır. Her bir kaynaştırılmış görüntü için elde edilen birinci temel bileşen Gabor doku özelliklerinin çıkartılması amacıyla kullanılmıştır. Son aşama olarak da elde edilen doku özellikleri girdi çok bantlı görüntüye eklenmiştir. Elde edilen bu görüntüler DVM algoritmasıyla sınıflandırılarak kullanılan metodolojinin sınıflandırma doğruluğunu ne derece etkilediği incelenmiştir. Sonuç olarak, GS yöntemiyle elde edilen Gabor doku özelliklerinin %19.3 artış ile sınıflandırma doğruluğunu en fazla oranda arttıran doku özelliği olduğu ve PCA yöntemiyle elde edilen Gabor doku özelliklerinin ise %18.7 artış ile sınıflandırma doğruluğunu en fazla oranda arttıran ikinci doku özelliği olduğu tespit edilmiştir.

References

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Improving SVM Classification Accuracy with Image Fusion-Based Gabor Texture Features

Year 2020, Volume: 1 Issue: 1, 34 - 44, 31.03.2020

Abstract

Image fusion is one of the most common techniques used to enhance the interpretability and functionality of remotely sensed data. The aim of this study was to improve the performance of the SVM (Support Vector Machines) classifier with the aid of texture features (TF) extracted from fused images. As a first step, the spatial resolution of the WorldView-2 MS (multispectral) imagery was increased by fusing it with a WorldView-2 PAN (panchromatic) image using the PCA (Principal Component Analysis), WSB (Wavelet Single Band), GS (Gram-Schmidt), BRV (Brovey), EHL (Ehlers), HCS (Hyperspherical Colour Space), HPF (High-Pass Filtering) and MCV (Multiplicative) algorithms. A PCA transform was then applied on all fused images. The first principal component obtained from each fused image was used to extract the Gabor TFs. As a final step, extracted Gabor TFs were combined with the original MS image. Resultant images were classified with the SVM algorithm to investigate to what degree the used methodology affect the classification accuracy. The results showed that the GS fusion-based Gabor TFs provided the greatest classification accuracy increase with 19.3%, whereas the PCA fusion-based Gabor TFs resulted in the second best classification accuracy increase with 18.7%.

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There are 75 citations in total.

Details

Primary Language English
Subjects Photogrammetry and Remote Sensing
Journal Section Research Articles
Authors

Çiğdem Şerifoğlu Yılmaz 0000-0002-9738-5124

Oguz Güngör 0000-0002-3280-5466

Publication Date March 31, 2020
Submission Date February 4, 2020
Acceptance Date March 16, 2020
Published in Issue Year 2020 Volume: 1 Issue: 1

Cite

APA Şerifoğlu Yılmaz, Ç., & Güngör, O. (2020). Improving SVM Classification Accuracy with Image Fusion-Based Gabor Texture Features. Türk Uzaktan Algılama Ve CBS Dergisi, 1(1), 34-44.