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Article
Publication date: 14 October 2019

Iwin Thanakumar Joseph S., Sasikala J. and Sujitha Juliet D.

The purpose of this paper is to study various ship detection methodologies. The accuracy of ship detection using satellite images still suffers from disturbances due to cluttered…

225

Abstract

Purpose

The purpose of this paper is to study various ship detection methodologies. The accuracy of ship detection using satellite images still suffers from disturbances due to cluttered scenes and varying ship sizes. The suitability of the techniques for various applications is explained in this survey.

Design/methodology/approach

A list of data on the subject was gathered and processed into tables. The test outcomes were then discussed to determine the most effective ship detection technique under various complex environments.

Findings

In this work, the advantages and disadvantages of different classification techniques of ship detection are highlighted. The suitability of the techniques for various applications is also explained in this survey. Several hybrid approaches can be developed in order to increase the accuracy of ship detection system. This survey also aids in highlighting the significant contributions of satellite images to effective ship detection system.

Originality/value

In this paper, studying various ship detection methodologies is given specific attention. A survey on ship detection and recognition is clarified with the detailed comparative analysis of various classifier techniques.

Details

International Journal of Intelligent Unmanned Systems, vol. 7 no. 4
Type: Research Article
ISSN: 2049-6427

Keywords

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Article
Publication date: 14 October 2019

Rajasekar Velswamy, Sorna Chandra Devadass, Karunakaran Velswamy and Jeyakrishnan Venugopal

The purpose of this paper is to classify the given image as indoor or outdoor with higher success rate by mixing various features like brightness, number of straight lines, number…

81

Abstract

Purpose

The purpose of this paper is to classify the given image as indoor or outdoor with higher success rate by mixing various features like brightness, number of straight lines, number of Euclidean shapes and recursive shapes.

Design/methodology/approach

For annotating an image, it is very easy, if the image is categorized as indoor or outdoor. Many methods are proposed to classify the given image in these criteria but still the rate of uncategorized images occupies considerable area. This proposed work is the extension of the existing works already proposed by experts in this field. Some of the parameters mainly focused to classify are color histogram, orientation of edges, straightness of edges, discrete cosine transform coefficients, etc. In addition to that, this work includes finding of Euclidean shapes i.e. closed contours and recursive shapes in the given image. When the Euclidean shaped object dominates the recursive shapes then it is classified as indoor object and if the recursive shapes dominates, it is categorized as outdoor object.

Findings

This work is carried out on the standard image data sets. The data sets are Microsoft Research Cambridge (MRC) object recognition image database 1.0. and Kodak and Coral image data set. Totally 540 images are taken into account and the images are classified 95.4 percent correctly.

Originality/value

Many methods are proposed to classify the given image in these criteria but still the rate of uncategorized images occupies considerable area. This proposed work is the extension of the existing works already proposed by experts in this field. Some of the parameters mainly focused to classify are color histogram, orientation of edges, straightness of edges, discrete cosine transform coefficients, etc. In addition to that, this work includes finding of Euclidean shapes i.e. closed contours and recursive shapes in the given image. When the Euclidean shaped object dominates the recursive shapes then it is classified as indoor object and if the recursive shapes dominates, it is categorized as outdoor object. This work is carried out on the standard image data sets. The data sets are MRC object recognition image database 1.0. and Kodak and Coral image data set. Totally 540 images are taken into account and the images are classified 95.4 percent correctly.

Details

International Journal of Intelligent Unmanned Systems, vol. 7 no. 4
Type: Research Article
ISSN: 2049-6427

Keywords

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