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

Öz

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.

Anahtar Kelimeler

• 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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