A Fast Ship Detection Algorithm Based on Automatic Censoring for Multiple Target Situations in SAR Images

EasyChair Preprint 7910

Abstract

Ship detection in Multi-Target Situations has become one of the crucial tasks in maritime surveillance. However, due to the existence of multiple Ship targets with different sizes in seacoast, standard Constant False Alarm Rate (CFAR) detection with a fixed guard window suffers from interfering targets within the training window. As a result, the probability of detection drops drastically. In this paper, a modified fast CFAR detection algorithm based on target indexing in multi-target situations is proposed. The detector does not require any guard window to prevent target interferences in the training window. It consists only of a training window and a test cell. It uses a novel interfering target indexing matrix based on a maximally stable extremal region (MSER) detector that provides the training window with the interference pixels locations to be censored. The Generalized Gamma distribution (GΓD) is adopted as the statistical model of sea clutter. Experimental results show that the proposed method could achieve effective detection results of ships in multi-target situations compared to CFAR detectors with fixed guard window size.

Keyphrases: Automatic Censoring, CFAR algorithm, Multi Target Situation, SAR images, Ship detection

@booklet{EasyChair:7910,
  author    = {Faical Farah and Toufik Laroussi and Hicham Madjidi},
  title     = {A Fast Ship Detection Algorithm Based on Automatic Censoring for Multiple Target Situations in SAR Images},
  howpublished = {EasyChair Preprint 7910},
  year      = {EasyChair, 2022}}