Evrişimsel sinir ağı ve iki-boyutlu karmaşık gabor dönüşümü kullanılarak hiperspektral görüntü sınıflandırma

Yıl 2020, Cilt: 35 Sayı: 1, 443 - 456, 25.10.2019

Kazım Hanbay

https://doi.org/10.17341/gazimmfd.479086

Cited By: 13

Öz

Bu çalışmada 2-boyutlu karmaşık Gabor filtreleme
ve derin evrişimsel sinir ağları kullanılarak yeni bir hiperspektral görüntü
sınıflandırma yöntemi önerilmiştir. Derin öğrenilen ve Gabor özellik çıkarma
metodolojileri giriş hiperspekral örnekler üzerinde eş zamanlı olarak
gerçekleştirilmiştir. Görüntülerin Gabor özellikleri çoklu yönelim ve
frekanslarda hesaplanır. Sonra derin özellikler ve Gabor özellikleri daha güçlü
ve ayırt edici özellik vektörü elde etmek için birleştirilir. Hibrit özellik
vektörü hiperspektral görüntü sınıflandırmak için softmax sınıflandırıcıya
giriş olarak kullanılır. İki hiperspektral veri seti üzerinde gerçekleştirilen
deneyler önerilen yöntemin bazı geleneksel yöntemlerden daha iyi sınıflandırma
performansı elde edebildiğini göstermiştir. 

Anahtar Kelimeler

Hiperspektral görüntü sınıflandırma, derin öğrenme

Kaynakça

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