Tarımsal alanlarda yüksek çözünürlüklü IKONOS uydu görüntüsünden nesne-tabanlı ürün deseni tespiti

Year 2019, Volume: 25 Issue: 5, 603 - 614, 21.10.2019

Beste Tavus Kamil Karataş , Mustafa Türker

Abstract

Günümüzde
uzaktan algılama teknolojisi ve görüntü işleme tekniklerin gelişmesiyle
birlikte, uydu görüntüleri tarımsal alanlarda ürün deseninin belirlenmesi
çalışmalarında sıklıkla tercih edilir hâle gelmiştir. Bu çalışmada, yüksek
konumsal çözünürlüklü IKONOS uydu görüntüsünden tarımsal alanlarda
nesne-tabanlı sınıflandırma yöntemi ile ürün desenin belirlenmesi
hedeflenmiştir. Çalışma alanı, Marmara Bölgesi’nde bulunan Bursa ili, Karacabey
ilçesinin güneybatısında yer almakta ve yaklaşık 9×9 km²’lik bir alanı
kapsamaktadır. Domates, mısır, biber, buğday, pirinç ve şeker pancarı bölgede
yetiştirilen başlıca tarım ürünleridir. Çalışmada, IKONOS uydu görüntüsü
çoklu-çözünürlük segmentasyon tekniği ile segmente edilmiştir. Segmentasyon
işleminde gerekli parametrelerden en önemlisi olan ölçek parametresi için en
uygun değer ESP-2 (Estimation of Scale Parameter) yazılımı ile belirlenmiştir.
Diğer segmentasyon parametreleri olan şekil ve bütünlük parametreleri için en
uygun değerler ise, yapılan deneme analizleri neticesinde tespit edilmiştir.
Sınıflandırmanın doğruluğunu artırmak için, görüntünün orijinal bantlarına ek
olarak, normalize edilmiş bitki indeksi (NDVI) bantı ile homojenlik, zıtlık,
farklılık, ortalama, varyans ve entropi doku bantları kullanılmıştır. Sınıflandırma
işlemi, toplam 29 bantlı veri seti kullanılarak eCognition yazılımında
nesne-tabanlı en yakın komşuluk tekniği ile yapılmıştır. Elde edilen
sınıflandırma sonucu, 2212 adet yer gerçeği verisi kullanılarak
değerlendirilmiştir. Doğruluk analizleri neticesinde, sınıflandırmanın genel
doğruluğu %87.5 olarak hesaplanmıştır. Elde edilen sonuçlar, yüksek
çözünürlüklü IKONOS uydu görüntüsünden tarımsal ürün deseni tespitinin
nesne-tabanlı sınıflandırma yöntemiyle yüksek doğrulukta belirlenebildiğini göstermektedir.

Keywords

Segmentasyon, Nesne-tabanlı sınıflandırma, Doku analizi

Object-based crop pattern detection from IKONOS satellite images in agricultural areas

Abstract

Nowadays,
with the development of remote sensing technologies and image processing
methods, satellite images have become frequently preferred in studies to
determine the crop pattern in agricultural areas. In this study, it is aimed to
detection the crop pattern in agricultural areas with high accuracy by using
object-based classification technique from high spatial resolution IKONOS
satellite images. The study area is located on the South-west of the Karacabey
district of the Bursa province in the Marmara Region and covers an area of
nearly 9×9 km². Tomato, corn, pepper, wheat, rice and sugar beet are
the main products grown in the region. In this study, the IKONOS satellite
image is segmented using multi-resolution segmentation technique. The most
appropriate value for the scale parameter, which is the most important
parameter in the segmentation process, has been determined by ESP-2 (Estimation
of Scale Parameter) software. Various combinations have been tried for shape
and compactness parameters in order to find the optimal segmentation
parameters. In order to increase classification accuracy, normalized difference
vegetation index (NDVI) and GLCM texture measurement methods have been used,
including homogeneity, contrast, dissimilarity, mean, variance, and entropy.
Using the data set from consist 29 bands, the image classification process have
been performed using the object-based nearest neighbor classification technique
in the eCognition software. The obtained classification results have been
tested on parcel basis using 2212 ground truth data. The overall accuracy of
the classification has been calculated as 87.5%. The results show that the high
spatial resolution IKONOS satellite image can be used to detection high
accuracy with object-based classification of agricultural crop pattern.

Keywords

Segmentation, Object-based classification, Texture analysis

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