AFET YÖNETİMİNDE OPTİK GÖRÜNTÜLER İÇİN DALGACIK DÖNÜŞÜMÜ VE GAUSSIAN KARIŞIM MODELLERİ TABANLI YENİ BİR DEĞİŞİM SAPTAMA YAKLAŞIMI

Orman yangını, taşkın gibi felaketler hem ülkemizin hem de dünyanın en önemli problemleri arasındadır Afet sonrası alansal rehabilitasyon işlemlerinin hızla yapılabilmesi için zarar gören alanların hızlı ve yüksek doğrulukla belirlenebilmesi gerekmektedir. Bu çalışmada, optik görüntüler için tasarlanmış, yangın ve sel gibi afetler sonrasında hasar gören alanların tespit edilmesine yönelik dalgacık dönüşümü ve Gaussian karışım modelleri temelli yeni bir yaklaşım önerilmiştir. Sunulan yaklaşımın ilk aşamasında, afet öncesi ve sonrasına ait görüntülerin standart ve logaritmik fark görüntüleri hesaplanır. İkinci aşamada standart fark görüntüsüne medyan filtre, logaritmik fark görüntüsüne wiener filtre uygulanır. Ardından bu görüntüler dalgacık dönüşümü ile birleştirilir. Son aşamada ise birleştirilmiş görüntü Gaussian karışım modelleri ile kümelenir ve böylelikle afet nedeniyle zarar gören alanlar tespit edilmiş olur. Yaklaşımın etkinliği gerçek afetler neticesinde ortaya çıkan Sardinia ve Mexico veri setleri kullanılarak irdelenmiştir. Önerilen yaklaşımın performansı toplam hata ve toplam hata oranı kriterlerine ek olarak ortalama karesel hata tepe gürültü sinyal oranı, yapısal benzerlik indeksi ve evrensel kalite indeksi metrikleri ile incelenmiş ve başarısı ortaya koyulmuştur.

Anahtar Kelimeler:

Afet, Değişim Saptama, Dalgacık Dönüşümü, Gaussian Karışım Modelleri

A NEW CHANGE DETECTION APPROACH BASED ON WAVELET TRANSFORMATION AND GAUSSIAN MIXTURE MODELS FOR OPTICAL IMAGERY IN DISASTER MANAGEMENT

Disasters such as forest fires and floods are among most important problems of both our country and the world. In order to be able to perform rapid rehabilitation processes after disaster, damaged areas should be determined with high accuracy quickly. In this study, a new approach, designed for optical images, based on wavelet transform and Gaussian mixture models is proposed for detection of damaged areas after disasters such as fire and flood. In the first step of the presented approach, standard and logarithmic difference images from images belonging before and after disaster are calculated. Second, median filter to standard difference image and wiener filter to logarithmic difference image are applied, respectively. After that, these images are fused with wavelet transformation. Lastly, fused image is clustered with Gaussian mixture models and thus the areas damaged by the disasters are identified. The effectiveness of the approach was explored using Sardinia and Mexico data sets resulting from real disasters. The performance of the proposed approach has been investigated and its success has been shown with the mean squared error, peak signal to noise ratio, structural similarity index and universal quality index metrics, in addition to the total error and total error rate criteria.

Keywords:

Disaster, Change Detection, Wavelet Transformation, Gaussian Mixture Models,

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