Automatic ship detection in single-pol SAR images using texture features in artificial neural networks |
Paper ID : 1079-SMPR-FULL |
Authors: |
Hamid Enayati1, Mehdi Modiri2, Mohammad Mohseni Aref3 1MSc degree of Photogrammetry, K.N.Toosi University of Technology 2professor of Maleke Ashtar University 3NO250 Farzangean Town Naranj Blvd |
Abstract: |
The interest in vessel monitoring and surveillance springs from the need to enforce regulations and be warned of security threats; it is for the most part authorities who have these needs. Regulations are related to maritime safety, immigration protection of the environment and natural resources, tariffs and duties, and public and workers health and well-being. Security threats can be related to piracy, terrorism, organized crime, and military and defence issues. SAR has the ability to penetrate clouds and provide information on both day and night. SAR imagery can potentially be used to detect a range of maritime activity, including small vessels, large ocean-going ships and even oil spills. SAR is a proven technology that can be used to detect ships at sea which have no active transponders (commonly reffered to as dark targets). Another advantage of using SAR is the large swath widths that these satellite based sensors can cover (thousands of square kilometers can be covered in a single pass) which reduces the monitoring cost per square kilometers significantly when compared to manual monitoring systems. Various methods have been proposed that process SAR images to monitor these targets. In this paper we propose a novel ship detection method .this method categorizes ship targets from single-pol SAR images using texture features in artificial neural networks. As such, the method tries to overcome the lack of an operational solution that is able to reliably detect ships with one SAR channel. The method has the following three main stages: 1) feature extraction; 2) feature selection; and 3) ship detection. The first part extracts different texture features from SAR image. These textures include occurrence and co-occurrence measures with different window sizes. Then, best features for ship detection are selected. Finally, the artificial neural network is used to extract ship pixels. In post-processing stage some morphological filters are used to improve the result. As sentinel images are free and have 5 meter spatial and 6 days temporal resolution,they can be used for ship monitoring.sentinel-1 SM image in VV polarization acquired for the surroundings of the Persian Gulf is used to test our algorithm. Although there is not any ground truth data for accuracy assessment of the results, but the visual results indicate that this algorithm can be implemented with time-saving, high precision ship extraction, feature analysis, and detection. The results also show that using texture features the algorithm properly discriminates speckle noise from ships. |
Keywords: |
ship detection, texture feature, neural network, Sentinel, SAR. |
Status : Paper Accepted (Poster Presentation) |