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A Correlation-Based Joint CFAR Detector Using Adaptively-Truncated Statistics in SAR Imagery.

Jiaqiu Ai1, Xuezhi Yang2, Fang Zhou3

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Summary

This study introduces a new correlation-based joint Constant False Alarm Rate (CFAR) detector for Synthetic Aperture Radar (SAR) images. The novel detector improves ship detection by using adaptively truncated statistics, enhancing probability of detection in cluttered environments.

Keywords:
2D joint log-normal distributionSARadaptively truncated clutter statisticscorrelation-based joint CFARship detection

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Area of Science:

  • Remote Sensing
  • Signal Processing
  • Image Analysis

Background:

  • Traditional Constant False Alarm Rate (CFAR) detectors struggle with probability of detection (PD) degradation in multiple target scenarios within Synthetic Aperture Radar (SAR) images.
  • Existing methods primarily rely on contrast information between targets and clutter, neglecting crucial correlation characteristics.

Purpose of the Study:

  • To propose a novel correlation-based joint CFAR detector, termed TS-2DLNCFAR, for improved SAR image analysis.
  • To enhance the probability of detection (PD) and reduce the false alarm rate (FAR) in complex, multi-target environments.

Main Methods:

  • Development of a two-dimensional (2D) joint log-normal model to represent the joint probability density function (JPDF) of clutter.
  • Implementation of an adaptive threshold-based clutter truncation method to remove outliers while preserving genuine clutter samples.
  • Accurate parameter estimation for the 2D joint log-normal model using adaptively truncated clutter statistics.

Main Results:

  • The proposed TS-2DLNCFAR detector demonstrates superior performance compared to traditional CFAR detectors.
  • Achieved high probability of detection (PD) and low false alarm rate (FAR) in scenarios with multiple targets.
  • Validated effectiveness on multi-look Envisat-ASAR and TerraSAR-X datasets.

Conclusions:

  • The TS-2DLNCFAR detector significantly improves SAR image target detection by exploiting gray intensity correlation.
  • Adaptive clutter truncation effectively handles outliers, leading to more robust and accurate detection.
  • The joint log-normal model provides a powerful framework for enhanced CFAR detection in challenging SAR imaging conditions.