Mixture Statistical Distribution Based Multiple Component Model for Target Detection in High Resolution SAR Imagery

Abstract

This paper proposes an innovative Mixture Statistical Distribution Based MultipleComponent (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.

Aircraft Detection from Low SCNR SAR Imagery Using Coherent Scattering Enhancement and Fused Attention Pyramid

“…Li et al [8] proposed a target detection algorithm to address the challenge in selecting a suitable SAR clutter statistical model based on double-domain sparse reconstruction saliency. He et al [9] proposed a multi-component model based on mixed statistical distribution, integrating both the target structure information and statistical distribution. However, these traditional feature extraction methods have a limited ability to excavate high-level semantic information of SAR images.…”

SAR Image Aircraft Target Recognition Based on Improved YOLOv5

“…Fu et al [1] proposed a method for aircraft recognition in SAR images based on the structural features of scattering and template matching, which used the Gaussian mixture structure to model the scattering characteristics of the target and improved the efficiency of template matching through a sample decision-optimization algorithm. He et al [2] proposed a mixed statistical-distribution-based multiple-component model 2 of 17 for target detection in high-resolution SAR imagery, which took both the structural information and the statistical distribution into account to achieve a better effect with respect to SAR aircraft detection. Dou et al [3] proposed an optimized target-attitude-estimation method, which used a multilayer neural network to obtain the prior shape information to reconstruct the SAR image.…”

Transitive Transfer Learning-Based Anchor Free Rotatable Detector for SAR Target Detection With Few Samples

“…Typical saliency includes template based saliency [7], gradient based saliency [8], region based saliency [9], and visual based saliency [10], [11], all of which require a certain amount of data to extract the saliency of a specific target. Shape and texture features based approaches describe the topological structure of the image and take advantage of different characteristics of the targets for detection [12]- [14]. This kind of methods extract features from target and background texture, which need to deal with different kinds of interference to make full use of texture features [15].…”