Aktarımlı Öğrenme ile SENTINEL-2 Görüntülerinden Kıyı Çizgisi Bölütlemesi

Öz

Kıyı çizgileri küresel ısınma, nüfus artışı, çevre kirliliği, kentleşme etkileriyle sürekli değişir. Doğal ve antropojenik etkilerle meydana gelen değişikleri tespit etmek için kıyı alanlarının izlenmesi gerekmektedir. Kıyı alanlarındaki değişimlerin sürdürülebilir bir şekilde izlenmesi, kıyı kaynak yönetimi, çevresel koruma ve planlama açısından oldukça önemli rol oynamaktadır. Uydu görüntüleri bu amaç için doğru, güvenilir, zamansal ve güncel bilgiler sağlamaktadır. Derin öğrenme(DL) ve aktarımlı öğrenme(TL) yaklaşımları kıyı çizgisi çıkartılmasında yeni olanaklar sağlamaktadır. Sunulan çalışmada, SENTINEL-2 görüntülerinden aktarımlı öğrenmeye dayalı, U-NET mimarisi kullanılarak, bir kara ve su bölütlemesi yaklaşımı önerilmiştir. Önceden eğitilmiş modele ait özellikler ve ağırlıklar için, LANDSAT-8 görüntüleri ile gerçekleştirilen derin öğrenme çalışmasından yararlanılmıştır. U-Net mimarisi kullanılan ağda, mavi, kırmızı ve yakın kızıl ötesi bantlarından oluşan tam çerçeve SENTINEL-2 görüntülerinden 8’i eğitim, 7’si test aşamasında kullanılmıştır. Tam çerçeve görüntüler 512x512 boyutlarında kırpılarak eğitim ve test için sırasıyla 115 ve 235 görüntü parçası oluşturulmuştur. Ortalama doğruluk, duyarlılık, hassasiyet, özgünlük ve F-skor değerleri sırasıyla 0.9917, 0.9927, 0.9908, 0.9907 ve 0.9917 olarak hesaplanmıştır. Çalışmanın sonuçlarına göre, aktarımlı öğrenme kullanılarak az miktarda görüntü ile yüksek doğruluklu kıyı çizgisi elde etmek mümkündür.

Anahtar Kelimeler

• Derin öğrenme
• Aktarımlı Öğrenme
• Kıyı Çizgisi Çıkartma
• SENTINEL-2
• LANDSAT-8

Shoreline Segmentation from SENTINEL-2 Imagery by Transfer Learning

Abstract

Global warming, increasing population, environmental pollution and urbanization can constantly affect coastal areas. Therefore, sustainable monitoring of coastal zones is vital to detect changes which can occur due to natural and anthropogenic effects. Thus, sustainable shoreline monitoring is essential for coastal resource management, environmental protection and planning. Satellite images provide accurate, reliable, temporal and up-to-date information for this purpose. State-of-the-art deep learning (DL) and transfer learning approaches brought new opportunities for shoreline extraction. In this study, a transfer learning based water-body segmentation framework with U-Net architecture from SENTINEL-2 imagery has been proposed. The pre-trained weights have been obtained from another study which is a network trained with LANDSAT-8 imageries. The training of used U-Net architecture was carried out using SENTINEL-2 imagery which consists of blue, red and NIR bands with 8 and 7 full frames for training and testing, respectively. Images have been cropped as 512x512 pixels and 115 and 235 patches have been created for the training and testing dataset, respectively. Average accuracy, recall, precision, specivity and F-score of the model values has been calculated as 0.9917, 0.9927, 0.9908, 0.9907 and 0.9917, respectively. The results show that it is possible to obtain shoreline with high accuracy with limited data using transfer learning.

Keywords

• Deep Learning
• Transfer Learning
• Shoreline Extraction
• SENTINEL-2
• LANDSAT-8

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