SAR Ship Detection Using Image Histograms and Machine Learning Approach

Kazım Hanbay; Mücahit Çalışan; Taha Burak Özdemir

Makale detayları

SAR Ship Detection Using Image Histograms and Machine Learning Approach

Yazarlar:

Kazım Hanbay | Mücahit Çalışan | Taha Burak Özdemir

Yayıncı:

Bingol University

Ana Başlıklar:

Bilgi Sistemleri Geliştirme Metodolojileri ve Uygulamaları

Dergi:

Türk Doğa ve Fen Dergisi Vol.13 Issue 3, pp.171-175

Belge Tipi:

Makale

Yayın Tarihi:

9/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001328020

pISSN:

2149-6366

Anahtar kelimeler:

Özet:

 Detection of objects through remote sensing is a current research area. Ship detection in SAR images is a very intense research area today. Convenience is provided to those concerned in applications such as military surveillance and tracking of commercial ships. In this study, ship images were classified using the Hessian matrix and HOG algorithm. Using the eigenvalues of the Hessian matrix, the angle and orientation information of the HOG method was calculated. Thus, distinctive pixel characteristics were coded. The proposed method has achieved successful results in experimental studies.

 Detection of objects through remote sensing is a current research area. Ship detection in SAR images is a very intense research area today. Convenience is provided to those concerned in applications such as military surveillance and tracking of commercial ships. In this study, ship images were classified using the Hessian matrix and HOG algorithm. Using the eigenvalues of the Hessian matrix, the angle and orientation information of the HOG method was calculated. Thus, distinctive pixel characteristics were coded. The proposed method has achieved successful results in experimental studies.

 Detection of objects through remote sensing is a current research area. Ship detection in SAR images is a very intense research area today. Convenience is provided to those concerned in applications such as military surveillance and tracking of commercial ships. In this study, ship images were classified using the Hessian matrix and HOG algorithm. Using the eigenvalues of the Hessian matrix, the angle and orientation information of the HOG method was calculated. Thus, distinctive pixel characteristics were coded. The proposed method has achieved successful results in experimental studies.

 Detection of objects through remote sensing is a current research area. Ship detection in SAR images is a very intense research area today. Convenience is provided to those concerned in applications such as military surveillance and tracking of commercial ships. In this study, ship images were classified using the Hessian matrix and HOG algorithm. Using the eigenvalues of the Hessian matrix, the angle and orientation information of the HOG method was calculated. Thus, distinctive pixel characteristics were coded. The proposed method has achieved successful results in experimental studies.

Makale detayları

SAR Ship Detection Using Image Histograms and Machine Learning Approach

Yazarlar:

Kazım Hanbay | Mücahit Çalışan | Taha Burak Özdemir

Yayıncı:

Bingol University

Ana Başlıklar:

Bilgi Sistemleri Geliştirme Metodolojileri ve Uygulamaları

Dergi:

Türk Doğa ve Fen Dergisi Vol.13 Issue 3, pp.171-175

Belge Tipi:

Makale

Yayın Tarihi:

9/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001328020

pISSN:

2149-6366

Remote sensing

Synthetic aperture radar

Machine learning

Eigenvalues

Sayı

Makale detayları

SAR Ship Detection Using Image Histograms and Machine Learning Approach

Yazarlar: Kazım Hanbay | Mücahit Çalışan | Taha Burak Özdemir

Yayıncı: Bingol University

Ana Başlıklar: Bilgi Sistemleri Geliştirme Metodolojileri ve Uygulamaları

Dergi: Türk Doğa ve Fen Dergisi Vol.13 Issue 3, pp.171-175

Belge Tipi: Makale

Yayın Tarihi: 9/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001328020

pISSN: 2149-6366

Remote sensing

Synthetic aperture radar

Machine learning

Eigenvalues

Sayı

Yazarlar:

Kazım Hanbay | Mücahit Çalışan | Taha Burak Özdemir

Yayıncı:

Bingol University

Ana Başlıklar:

Bilgi Sistemleri Geliştirme Metodolojileri ve Uygulamaları

Dergi:

Türk Doğa ve Fen Dergisi Vol.13 Issue 3, pp.171-175

Belge Tipi:

Makale

Yayın Tarihi:

9/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001328020

pISSN:

2149-6366