Yüksek Çözünürlüklü İnsansız Hava Aracı Görüntülerinde Bitki Gölgelerinin Tespit Edilmesi

Year 2021, Volume: 36 Issue: 2, 189 - 199, 15.06.2021

Emre Tunca Eyüp Köksal

https://doi.org/10.7161/omuanajas.826960

Abstract

Yüksek çözünürlüklü multispektral görüntüler tarım alanlarının izlenmesinde oldukça yararlı bilgiler sunmaktadır. Fakat bu görüntülerdeki gölge alanları spektral yansıma oranı ve termal verileri direk olarak etkilemektedir. Bu nedenle gölge alanlarının tespit edilmesi ve filtrelenmesi, yüksek çözünürlüklü görüntülere dayalı olarak gerçekleştirilen çalışmalardaki başarı oranını arttıracaktır. Görüntülerde bulunan gölge alanları sınıflandırma yöntemleri ile tespit edilmektedir. Fakat, bu yöntemlerin insansız hava aracından (İHA) elde edilen yüksek çözünürlüklü multispektral görüntülerde kullanımı üzerine gerçekleştirilmiş araştırma sayısı oldukça azdır. Bu çalışmanın amacı üç farklı görüntü sınıflandırma yönteminin (eğitimli sınıflandırma, multispektral görüntü ile sınıflandırma ve sınıf olasılığı) İHA’ dan elde edilen multispektral görüntülerdeki gölge alanlarını tespit etmedeki başarısının değerlendirilmesidir. Her bir sınıflandırma yönteminin başarısı, görüntüde manuel yöntem ile belirlenen gölge alanları ile değerlendirilmiştir. Çalışma sonucunda bitki gölgeleri en hassas (%94) kırmızı kenar multispektral görüntüsü kullanılarak gerçekleştirilen sınıflandırma ile elde edilirken, en düşük hassasiyet (%74) ise eğitimli sınıflandırma yöntemi ile hesaplanmıştır.

Keywords

Görüntü Sınıflandırma, Bitki Gölge Tespiti, Multispektral

Supporting Institution

Ondokuz Mayıs Üniversitesi

Project Number

PYO.ZRT.1904.19.001

Thanks

Bu çalışma Ondokuz Mayıs Üniversitesi tarafından desteklenmiştir (PYO.ZRT.1904.19.001).

Crop Shadow Detection in High Resolution Un-Manned Air Vehicle Images

Abstract

High-resolution multispectral image provides useful information for monitoring agricultural areas. However, shadow areas in these images directly affect spectral reflectance and thermal data. For this reason, detecting and removing of shadow areas will increase the success rate of studies that performed based on high resolution images. Shadows can be detected by using image classification methods. However, researches related to use of these methods in high resolution images obtained from un-manned air vehicles are quite limited. Therefore, the aim of this study is evaluation of three different image classification methods (supervised classification, multispectral image classification and class probability) for detecting shadows of bell pepper plant. Each classification method was compared with the shadow areas which manually determined in the image. The results show that, while the most accurately bell pepper shadows detected by using Red Edge multispectral image (94%), the lowest with supervised classification (74%).

Keywords

Multispectral, Image Classification, Shadow Detection

Project Number

PYO.ZRT.1904.19.001

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