Gelişmiş Deniz Gözlemi: SAR Tabanlı Gemi Tespiti için CNN Algoritmalarının Kullanımı

Year 2023, Volume: 5 Issue: 1, 1 - 7, 30.06.2023

Halil İbrahim Şenol

https://doi.org/10.51946/melid.1267997

Abstract

Deniz gözetiminde gemilerin tespiti, önemli pratik uygulamaları olan temel bir araştırmadır. Bu çalışmada Sentinel-1 verilerinin ve Faster R-CNN algoritmalarının gemi tespiti için kullanımını araştırdım ve %86.11 doğruluk elde ettim. Faster R-CNN algoritması, görüntülerdeki nesneleri algılamada olağanüstü performans sergileyen, derin öğrenmeye dayalı bir nesne algılama çerçevesidir. Sentinel-1, Avrupa Uzay Ajansı tarafından işletilen ve hassas mekansal çözünürlüğe sahip Sentetik Açıklıklı Radar (SAR) görüntüleri sağlayan ve onu gemi tespit uygulamaları için çok uygun hale getiren bir radar uydusudur. Önerilen metodoloji, doğru gemi tespiti için Sentinel-1 verilerini Faster R-CNN algoritması ile birleştirmenin etkinliğini göstererek, deniz gözetimi ve gemi trafiği yönetimindeki pratik uygulamalar için potansiyeli vurgulamaktadır. Çalışmanın sonuçları, deniz taşımacılığının emniyet ve güvenliğinin iyileştirilmesine katkıda bulunabilir ve denizcilik alanındaki çok çeşitli operasyonel ve araştırma faaliyetlerini desteklemeye yardımcı olabilir.

Keywords

Uzaktan algılama, SAR, Sentinel-1, Faster R-CNN, derin öğrenme

Advancing Maritime Surveillance: Using CNN Algorithms for SAR-based Ship Detection

Abstract

The detection of ships in maritime surveillance is an essential task with significant practical applications. In this study, I investigated the use of Sentinel-1 data and Faster R-CNN algorithms for ship detection, achieving an accuracy of 86.11%. The Faster R-CNN algorithm is a deep learning-based object detection framework that has demonstrated outstanding performance in detecting objects in images. Sentinel-1 is a radar satellite operated by the European Space Agency that provides Synthetic Aperture Radar (SAR) images with excellent spatial resolution, making it well-suited for ship detection applications. The proposed methodology showcases the effectiveness of combining Sentinel-1 data with the Faster R-CNN algorithm for accurate ship detection, highlighting the potential for practical applications in maritime surveillance and vessel traffic management. The study's results can contribute to improved safety and security of sea transport and can help support a wide range of operational and research activities in the maritime domain.

Keywords

Remote sensing, SAR, Sentinel-1, Faster R-CNN, deep learning

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