Automatic Image Contrast Enhancement for Small Ship Detection and Inspection Using RADARSAT-2 Synthetic Aperture Radar Data

  • Author(s): Jaehoon Jeong and Chan-Su Yang
  • DOI: 10.3319/TAO.2016.01.01.01(ISRS)
  • Keywords: Synthetic aperture radar, Ship detection, Contrast enhancement, Automation, Power-law scaling
Abstract

This study devises an automatic synthetic aperture radar (SAR) image enhancement method for ship detection and inspection for installation in a near-real-time automatic high-speed processing system. The proposed method was examined in small ship inspection and detection over the Ieodo Ocean area off Korea using RADARSAR-2 HV-polarization data. The proposed method involves four steps. First, the SAR input data is converted into a highly compressed gray scale image, which enables both computer screen display and high-speed processing due to its light volume. Second, the overall contrast is adjusted by power-law scaling to strengthen the target discrimination, which is attenuated because of the inefficiency of one-sided intensity distribution. This additionally provides excellent target visibility. Third, the intensity of the area in which targets and clutter coexist is rescaled from 0 to 255 using min-max linear stretching. This suppresses background clutter and makes targets more easily distinguishable from the clutter. Lastly, the remaining clutter is successfully eliminated using a median filter. As a result, an output image is obtained that is close to binary data and enables ship detection using only simple global thresholding. Our ship detection results were compared with that of ships identified using an automatic identification system and those visible in high-precision images by visual inspection. We verified that our method offers a high detection rate for small ships and does not involve complexity in distribution assumption, filtering or thresholding. The potential of our method is confirmed as an automatic SAR enhancement method for near-real-time ship detection and inspection.

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