Abstract
This paper proposes an innovative Mixture Statistical Distribution Based Multiple Component (MSDMC) model for target detection in high spatial resolution Synthetic Aperture Radar (SAR) images. Traditional detection algorithms usually ignore the spatial relationship among the target’s components. In the presented method, however, both the structural information and the statistical distribution are considered to better recognize the target. Firstly, the method based on compressed sensing reconstruction is used to recover the SAR image. Then, the multiple component model composed of a root filter and some corresponding part filters is applied to describe the structural information of the target. In the following step, mixture statistical distributions are utilised to discriminate the target from the background, and the Method of Logarithmic Cumulants (MoLC) based Expectation Maximization (EM) approach is adopted to estimate the parameters of the mixture statistical distribution model, which will be finally merged into the proposed MSDMC framework together with the multiple component model. In the experiment, the aeroplanes and the electrical power towers in TerraSAR-X SAR images are detected at three spatial resolutions. The results indicate that the presented MSDMC Model has potential for improving the detection performance compared with the state-of-the-art SAR target detection methods.
Highlights
Target detection in Synthetic Aperture Radar (SAR) imagery is an important application in remote sensing research
The Single Distribution Based Multiple Component (SDMC) model can provide complete information about the target and the accurate location is shown with a bounding box, which suggests that modeling the structure information is beneficial to describe the target in high spatial resolution SAR images
This paper has presented a new Mixture Statistical Distribution Based Multiple Component (MSDMC) model for object detection in high spatial resolution SAR images
Summary
Target detection in Synthetic Aperture Radar (SAR) imagery is an important application in remote sensing research. Numerous algorithms have been developed in response to the need for target detection in SAR imagery with different spatial resolutions. In low spatial resolution SAR imagery, Constant False Alarm Rate (CFAR) [1,2], in which an adaptive threshold is adopted, has become the most widely used method. In [3], the statistical distribution of the background clutter is assumed to be Gaussian to obtain the adaptive threshold. Based on the contrast between the target and the background, the Generalized Likelihood Ratio (GLR) [4] method has been proposed based on the clutter’s statistical distribution features to achieve an optimal solution under a Bayesian framework. In medium spatial resolution SAR imagery, linear targets, such as oceans and oil spills, can be detected using a Radon transform method [5,6]. To enhance the discrimination between the target and the background, Kaplan [7]
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