Hiperspektral görüntülerde Relief-F algoritması ile band seçimi

Yıl 2024, , 766 - 775, 15.07.2024

Mehmet Yılmaz , Ümit Haluk Atasever

https://doi.org/10.28948/ngumuh.1408200

Öz

Hiperspektral görüntüler, sınıflandırması için detaylı bilgi içermektedirler. Ancak bu veriler yüksek boyut, büyük veri hacmi ve bitişik bantlar arasındaki güçlü korelasyon özellikleri nedeniyle sınıflandırma sonuçlarını olumsuz etkilenmektedir. Uygun bir öznitelik seçim yöntemi ile hiperspektral görüntülerin sınıflandırma etkinliği ve doğruluğu iyileştirilebilir. Bu çalışmada sınıflandırma modelinden bağımsız olması, çoklu bağlantı varsayımını dikkate almaması, gürültü değerlerini işleyebilmesi gibi özellikleri nedeniyle Relief-F öznitelik seçme algoritması tercih edilmiştir. Relief-F algoritmasının uygulama etkisini incelemek için Salinas-A, Indian Pines ve Pavia University veri setleri, deneysel veri olarak kullanılmıştır. Gerçekleştirilen uygulamalar sonrasında band seçimi sonrası Salinas-A, Indian Pines verisetlerinde Destek Vektör Makineleri sınıflandırıcısının daha yüksek performans gösterirken; Rastgele Orman yöntemininin sınıflandırma doğruluğunun büyük oranda korunduğu görülmüştür. Araştırma sonuçları, Relief-F algoritmasının hiperspektral görüntülerde en gerekli özelliklerini belirlemek ve iyi bir sınıflandırma doğruluğu ile bant sayısının %60 - %70 azaltılabileceği göstermektedir.

Anahtar Kelimeler

Hiperspektral görüntü sınıflandırma, Öznitelik seçimi, Relief-F

Band selection with Relief-F algorithm in hyperspectral ımages

Öz

Hyperspectral images contain detailed information for classification. However, these data negatively affect the classification results due to their high size, large data volume and strong correlation between adjacent bands. Classification efficiency and accuracy of hyperspectral images can be improved with an appropriate feature selection method. In this study, the Relief-F feature selection algorithm was preferred due to its features such as being independent of the classification model, not taking into account the assumption of multicollinearity, and being able to process noise values. Salinas-A, Indian Pines and Pavia University datasets were used as experimental data to examine the application effect of the Relief-F algorithm. After the applications, the Support Vector Machine classifier showed higher performance in the Salinas-A and Indian Pines datasets after band selection; It has been observed that the classification accuracy of the Random Forest method is largely preserved. The research results show that the Relief-F algorithm determines the most necessary features in hyperspectral images and the number of bands can be reduced by 60% - 70% with a good classification accuracy.

Anahtar Kelimeler

Hyperspectral image classification, feature selection, Relief-F

Kaynakça

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