SHIP DETECTION IN SATELLITE IMAGES WITH MASK R-CNN

Öz

The recent breakthrough in the field of deep learning has led to significant progress in the detection of ships in remote sensing images. In particular, convolutional neural networks (CNN), an approach developed for object detection and classification, has been successfully and widely used for ship detection. In addition, the improved quality of satellite imagery has made it possible to detect and distinguish ships and even smaller objects from this data. In this study, Mask R-CNN, a region-based convolutional neural network model, is used to detect ships in optical satellite images. The main objective of the study is to investigate the performance and limitations of the model in detecting ships in satellite images. For this purpose, 1838 satellite images with a spatial resolution of 1 meter of the areas where ships are densely populated were downloaded and data sets were created by labelling the ships in the content with masks through a GIS software. The results show that the proposed method is capable of successfully detecting ships even in challenging contexts, but there are some limitations in the model in its current form. In particular, the model is inaccurate in detecting ships located close to each other.

Anahtar Kelimeler

• Deep Learning
• Mask R-CNN
• Satellite Images
• Ship Detection

MASK R-CNN İLE UYDU GÖRÜNTÜLERİNDE GEMİ TESPİTİ

Öz

Derin öğrenme alanında son yıllarda gerçekleşen atılımın katkısıyla uzaktan algılama görüntülerinde gemi tespiti konusunda önemli ilerlemeler kaydedilmiştir. Özellikle, nesne tespiti ve sınıflandırması için geliştirilen yaklaşımlardan olan konvolüsyonel sinir ağları (CNN) gemi tespiti için de başarılı ve yaygın bir şekilde kullanılır hale gelmiştir. Bunun yanında, uydu görüntülerinin niteliklerinin gelişmesi bu verilerden gemilerin ve hatta daha küçük nesnelerin algılanıp ayırt edilebilmesinin önünü açmıştır. Bu çalışmada, optik uydu görüntülerindeki gemileri tespit etmek üzere bölge-tabanlı konvolüsyonel sinir ağı modellerinden biri olan Mask R-CNN yöntemi kullanılmıştır. Çalışmadaki temel amaç, kullanılan modelin uydu görüntülerindeki gemi tespit performansını ve sınırlarını incelemektir. Bunun için, gemilerin yoğun olarak bulunduğu alanların 1 metre mekânsal çözünürlüğe sahip 1838 adet uydu görüntüsü indirilmiş ve içerikteki gemiler bir GIS yazılımı aracılığıyla maskelerle etiketlenerek veri setleri oluşturulmuştur. Elde edilen sonuçlar, önerilen yöntemin zorlu içeriklerde bile gemileri başarıyla tespit etme kabiliyetinde olduğunu fakat mevcut haliyle modelde bazı kısıtlamalar bulunduğunu göstermiştir. Model özellikle birbirine yakın konumlanmış gemilerin tespitinde yanılgıya düşmektedir.

Anahtar Kelimeler

• Derin Öğrenme
• Gemi Tespiti
• Mask R-CNN
• Uydu Görüntüleri

Kaynakça

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