A Novel Algorithm for Ship Detection in SAR Imagery Based on the Wavelet Transform

Tello, M.; López-Martínez, Carlos; Mallorquí, J.J.

doi:10.1109/lgrs.2005.845033

Cited by 243 publications

(133 citation statements)

References 5 publications

Supporting

Mentioning

121

Contrasting

Order By: Relevance

“…If the SAR raw data contain a moving target, the processed SAR has unfocused and/or displaced areas [5]. To detect moving targets in single-channel SAR data, researchers have proposed many methods based on the spectrum filtering algorithm [6,7], shear average algorithm [8], RDM (reflectivity displacement method) algorithms [9], time-frequency analysis algorithm [5,10], sub-aperture algorithm [11], keystone transform algorithm [12], wavelet transform algorithm [13,14], and so on. To evaluate the performance of these methods, the researchers need various SAR raw data containing moving targets with different velocities in different directions, clutter model types, different noise models, etc.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Method for Simulation of Synthetic Aperture Radar (SAR) Raw Data in Moving Target Detection

Rahmanizadeh

Amini

2017

Remote Sensing

View full text Add to dashboard Cite

SAR (Synthetic Aperture Radar) raw data simulation has a significant role in the evaluation of newly-proposed methods for the estimation of moving target parameters. The evaluation of methods in different cases emphasizes the importance of the need to have fast simulators. Using reverse SAR imaging methods for the raw data simulations has achieved good results in the simulation of the static targets, but for the simulation of moving targets these methods have a few shortcomings. In this paper, we propose a method to simulate a speckled scene with moving targets in the hybrid domain. First, the scene is simulated, including speckle, which is statistically in accordance with real SAR image behavior. Then, a reverse imaging algorithm (inverse chirp scaling) was applied on the scene to generate the SAR raw data. The moving target simulation was also done in the time-domain as the next step. Finally, the results from two prior steps were superposed to generate the SAR raw data with moving targets. All steps of the proposed method were evaluated separately. The speckle procedure was evaluated by comparing the speckled SAR image before the simulation and the image of the SAR simulated raw data. The results show similar variations in real and imaginary parts of these data. The correlation between the reflectivity map and the SAR images after the simulation was calculated and the obtained correlation coefficient was about 0.95 for different images. The final data were further analyzed for the displacement of moving targets' positions. The results show similar displacement between moving target SAR raw data with a background and without a background.

show abstract

Section: Introductionmentioning

confidence: 99%

An Integrated Method for Simulation of Synthetic Aperture Radar (SAR) Raw Data in Moving Target Detection

Rahmanizadeh

Amini

2017

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…To enhance the discrimination between the target and the background, Kaplan [7] proposed the Extended Fractal (EF) feature, which introduced the Hurst index to measure texture roughness at different scales. By contrast, Tello [8] utilized the different characteristics of a target in the spatial and frequency domains to improve the detection results. Wang [9] employed the sub-aperture correlation method for object detection in polarimetric SAR data.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

et al. 2017

IJGI

View full text Add to dashboard Cite

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.

show abstract

“…Exploiting the fact that in SAR images ships appear as clusters of bright pixels against the darker background of sea clutter, targets are basically selected by defining a detection threshold based on image statistics. The constant false alarm rate (CFAR) approach (Eldhuset 1996;Wackerman et al 2001) has been and still is the most widely used method, although in the literature a number of alternative techniques can be found, e.g., wavelet-based approaches (Tello, Lopez-Martinez, and Mallorqui 2005), the modified Otsu's algorithm (Messina et al 2012) or the model-based scattering power decomposition (Sugimoto, Ouchi, and Nakamura 2013) for multi-polarization images.…”

Section: Introductionmentioning

confidence: 99%

The exploitation of Sentinel-1 images for vessel size estimation

Stasolla

Greidanus

2016

Remote Sensing Letters

View full text Add to dashboard Cite

In this article, a novel technique for fully automatic vessel size estimation using medium-to-high-resolution synthetic aperture radar (SAR) images is presented. Based on mathematical morphology, it aims at better delineating the vessel outline in the cluttered SAR image, thereby enabling the extraction of its actual dimensions. The technique has been tested on a set of 127 ships representing a range in lengths between 24 and 366 m in five Sentinel-1 images at 20 m multilook resolution that have good quality ground truth available. It is found that the proposed algorithm produces very good length estimates (15% relative error/30 m absolute error) and reasonable width estimates (35%/11 m). The estimates are significantly better than those from a simpler automatic method that does not use mathematical morphology, and approach those from manual analysis by an expert. ARTICLE HISTORY

show abstract

A Novel Algorithm for Ship Detection in SAR Imagery Based on the Wavelet Transform

Cited by 243 publications

References 5 publications

An Integrated Method for Simulation of Synthetic Aperture Radar (SAR) Raw Data in Moving Target Detection

An Integrated Method for Simulation of Synthetic Aperture Radar (SAR) Raw Data in Moving Target Detection

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

The exploitation of Sentinel-1 images for vessel size estimation

Contact Info

Product

Resources

About