Ship Targets Discrimination Algorithm in SAR Images Based on Hu Moment Feature and Texture Feature

Lei Liu,Hai-Bo Zhang,Ting-Jun Li,Qi Feng,Yang Zhang

doi:10.1051/itmconf/20160702008

Lei Liu, Hai-Bo Zhang + Show 3 more

Open Access

https://doi.org/10.1051/itmconf/20160702008

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Abstract

To discriminate the ship targets in SAR images, this paper proposed the method based on combination of Hu moment feature and texture feature. Firstly,7 Hu moment features should be extracted, while gray level co-occurrence matrix is then used to extract the features of mean, variance, uniformity, energy, entropy, inertia moment, correlation and differences. Finally the k-neighbour classifier was used to analysis the 15 dimensional feature vectors. The experimental results show that the method of this paper has a good effect.

Highlights

Marine environmental monitoring has become the common concern for all countries in the world [1].Ships are the main tools to develop Marine, so how to effectively get the ship information is becoming a hot spot of current research
Combining with 7 Hu invariant moments features and 8 texture feature extraction to form the feature vector 15d, and k-neighbor classifier are classified, and separately adopt the Hu invariant moment features and texture features of the classification results are compared, the results show that this algorithm can effectively improve the accuracy of classification [8,9]
This paper proposed a ship targets discrimination algorithm in SAR images based on Hu moment feature and texture feature

Summary

Introduction

Marine environmental monitoring has become the common concern for all countries in the world [1].Ships are the main tools to develop Marine, so how to effectively get the ship information is becoming a hot spot of current research. Automatic target recognition (ATR) system can extract information of ship targets in SAR image, mainly includes three phases: detection, discrimination and classification [2]. The detection phase can remove the region that can't be the target area, while discrimination phase can remove natural clutter and artificial clutter false alarm, the classification phase is to classify ship target recognition. Feature extraction is the foundation of SAR images of ship target identification [4,5]. In the late 1980s, Burl and Novak firstly put forward by using texture feature for SAR image target discrimination in Lincoln laboratory, in the 30 years, scholars from all over the world had made many achievements in the discrimination of feature extraction [6]. Combining with 7 Hu invariant moments features and 8 texture feature extraction to form the feature vector 15d, and k-neighbor classifier are classified, and separately adopt the Hu invariant moment features and texture features of the classification results are compared, the results show that this algorithm can effectively improve the accuracy of classification [8,9]

Hu Moment Feature Extraction

Texture Feature Extraction based on Gray Level Co-occurrence Matrix

Classification Method

Result of the Experiment and Analysis

Conclusion