Shearlet dönüşüm ve yeni geometrik özellikler kullanılarak aşırı öğrenme makinesine dayalı bitki tanıma sistemi

Yıl 2019, Cilt: 34 Sayı: 4, 2097 - 2112, 25.06.2019

Muammer Türkoğlu , Davut Hanbay

https://doi.org/10.17341/gazimmfd.423674

Cited By: 3

Öz

Bitki türlerini doğru tespit
edebilmek için bugüne kadar yapılan çalışmalarda farklı yaklaşımlar
kullanılmıştır. Bu yaklaşımlardan en temel olan bitki yaprakları, şekil, renk
ve damar dokusu gibi avantajlara sahip birçok özellik içermektedir. Bu
çalışmada açıdan bağımsız olarak yaprağın geometrik özelliklerine dayalı yeni
bir yaklaşım önerilmiştir. Kenar Adım (KA) olarak adlandırılan bu yöntem,
şeklin sınır eğrilerindeki kenar noktalar kullanılarak açı, merkez-kenar
uzunluğu ve kenar mesafesi gibi özelliklerden oluşmaktadır. Ayrıca doku
tanımada iyi hassasiyet göstermesi, hızlı hesaplama yapması ve yön bağımsızlığı
gibi özelliklere sahip olan Shearlet Dönüşüm yöntemi kullanılmıştır. Bu
yöntemlere ek olarak renk özellikleri ile Gri Seviye Eş-Oluşum Matrisleri
(GSEM) yöntemi uygulanmıştır. Tüm bu yöntemlerden elde edilen öznitelikler ayrı
ayrı ve bileşik olarak Aşırı Öğrenme Makineleri (AÖM) sınıflandırıcı yöntemi
ile test edilmiştir. Flavia, Swedish, ICL ve Foliage gibi dört farklı bitki
yaprak veri setleri kullanılarak önerilen çalışma test edilmiştir. Bu veri
setleri kullanılarak doku, şekil ve renk özelliklerine dayalı yapılan
çalışmalar ile önerilen yaklaşımın performansı kıyaslanmıştır. Sonuç olarak,
önerilen çalışmanın diğer çalışmalara göre daha başarılı olduğu tespit
edilmiştir.

Anahtar Kelimeler

Yaprak Tanıma, Görüntü İşleme, Shearlet Dönüşüm, Kenar Adım Yöntemi, Aşırı Öğrenme Makinesi

Kaynakça

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