The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-1/W5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 395–399, 2015
https://doi.org/10.5194/isprsarchives-XL-1-W5-395-2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 395–399, 2015
https://doi.org/10.5194/isprsarchives-XL-1-W5-395-2015

  11 Dec 2015

11 Dec 2015

AUTOMATIC SHIP DETECTION IN SINGLE-POL SAR IMAGES USING TEXTURE FEATURES IN ARTIFICIAL NEURAL NETWORKS

E. Khesali1, H. Enayati1, M. Modiri2, and M. Mohseni Aref3 E. Khesali et al.
  • 1K.N.Toosi University of Technology, Mirdamad, Tehran, Iran
  • 2Maleke Ashtar University, Isfahan, Iran
  • 3Istanbul Technical University, Istanbul, Turkey

Keywords: Ship detection, texture feature, neural network, sentinel, SAR

Abstract. This paper presents a novel method for detecting ships from high-resolution synthetic aperture radar (SAR) images. 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 are selected. Finally, the artificial neural network is used to extract ship pixels from sea ones. In post processing stage some morphological filters are used to improve the result. The effectiveness of the proposed method is verified using Sentinel-1 data in VV polarization. Experimental results indicate that the proposed 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.