Ship detection is significant especially with the increasing worldwide cooperation in commerce and military affairs.
Space-borne Synthetic Aperture Radar (SAR) is optimal for ship detection due to its high resolution over wide swaths
and all-weather working capability. Constant False Alarm Rate (CFAR) detection of ships in SAR imagery is a robust
and popular choice. K distribution has been widely accepted for homogeneous sea clutter modeling. Although localized
K-distribution based CFAR detection has been developed to solve the non-homogeneous problem, it is not satisfied
under adverse conditions, for example, interference target appears in the background window. In order to overcome its
shortcomings, this paper presents an adaptive algorithm to improve the performance. It mainly includes the homogeneity
assessment of the local background area and the automatic selection between the localized K-distribution-based CFAR
detector and the OS-CFAR detector, which has better detecting performance in non-homogeneous situation. The theory
is investigated in detail firstly, and then experiments are carried out and the results illustrate that the novel algorithm
outperforms the state-of-art methods especially under complex sea background condition.
As a method of representing the test sample with few training samples from an overcomplete dictionary, sparse representation has attracted much attention in target classification recently. In this paper, we develop a new SAR vehicle classification method based on sparse representation, in which the correlation between the vehicle’s aspect angle and the sparse representation coefficients is exploited. The detail procedure presented in this paper can be summarized as follows. Initially, the sparse coefficient vector of a test sample is solved by sparse representation algorithm with a pixel based dictionary. Then the coefficient vector is projected onto a sparser one with the constraint of vehicle’s aspect angle. Finally, the vehicle is classified to a certain category that minimizes the reconstruct error with the sparse coefficient vector. We present promising results of applying the proposed method to the MSTAR dataset.
Precise segmentation is crucial for the feature extraction and classification of ships in SAR imagery. To alleviate the Doppler shift and the cross ambiguity, this paper propose to segment the ship area from its background based on the radon transform. Assuming that the region of interest (ROI) of ship in SAR imagery has been extracted, the detail procedures of the proposed refined segmentation can be summarized as follows. First, the ship’s ROI image is transformed to radon domain, in which pixel intensities are cumulated along different directions. Then, the peak areas are separated to extract the ship’s orientation and the main image area of the ship that orthogonal to the principal axis. Finally, the refined segmentation is achieved in the main image area. Experiments, accomplished over measured medium and high resolution SAR ship images, show the effectiveness of the proposed approach.
Orientated towards the application of ship detection in SAR imagery, several typical distributions used for describing the
single look synthetic aperture radar (SAR) amplitude imagery and their parameter estimation methods have been
summarized. Using the histogram fitting method, this paper analyzes the SAR sea clutter's statistical characteristics and
carries out some statistical modeling experiments based on numerous measured SAR images. The results indicate that
the log-normal and amplitude-K distributions are suitable to model the experimental data among the several distributions.
These two distributions have almost the same modeling precision, but the former one's calculation is more efficient.