Jose-Luis Barcena-Humanes

Statistical Analysis of SAR Sea Clutter for Classification Purposes

Statistical analysis of radar clutter has always been one of the topics, where more effort has been put in the last few decades. These studies were usually focused on finding the statistical models that better fitted the clutter distribution; however, the goal of this work is not the modeling of the clutter, but the study of the suitability of the statistical parameters to carry out a sea state classification. In order to achieve this objective and provide some relevance to this study, an important set of maritime and coastal Synthetic Aperture Radar data is considered. Due to the nature of the acquisition of data by SAR sensors, speckle noise is inherent to these data, and a specific study of how this noise affects the clutter distribution is also performed in this work. In pursuit of a sense of wholeness, a thorough study of the most suitable statistical parameters, as well as the most adequate classifier is carried out, achieving excellent results in terms of classification success rates. These concluding results confirm that a sea state classification is not only viable, but also successful using statistical parameters different from those of the best modeling distribution and applying a speckle filter, which allows a better characterization of the parameters used to distinguish between different sea states.

Neural network based solutions for ship detection in SAR images

Ship detection is nowadays quite an important issue in tasks related to sea traffic control, fishery management and ship search and rescue. Although it has traditionally been carried out by patrol ships or aircrafts, coverage and weather conditions can become a problem. Synthetic aperture radars can surpass these coverage limitations and work under any climatological condition. Two ship detectors are proposed in this paper. The first one is a MLP-based detector that uses K-distribution parameters to characterize the sea clutter and the brightness of the pixels to detect ships. The second one is the double parameters model, DPM, proposed in the literature. While the DPM-based detector gives rise to some false alarms, leading to the need of a discrimination stage, and has some troubles when the ships are in rough water, the MLP-based detector along with the combination of both sea and ship features obtains better results in terms of detection and false alarm rates.

Robustness of a Generalized Gamma CFAR ship detector applied to TerraSAR-X and Sentinel-1 images

A fast CFAR ship detector based on the statistical modeling of sea clutter in SAR images is proposed. Typical CFAR detectors, like the double parameter model (DPM), assume a Gaussian sea clutter model and usually degrade the image resolution using target windows. The proposed detector works in a pixel-by-pixel fashion, adaptively selecting a decision threshold for every single patch the SAR image is divided into. Therefore, the presence of different-sized ships will not become an issue and they will be better characterized, allowing the extraction of the target information, which could be used for refocusing, feature extraction, modeling and classification, while maintaining the resolution of the SAR image. Sea clutter is studied using different statistical models, with the Generalized Gamma distribution presenting itself as the most suitable one. This model is used to characterize the sea clutter in the proposed detector. In order to showcase the robustness of the proposed detector, images acquired with SAR sensors working in different frequency bands are selected. Ship detections results show a good performance regardless of the sensor, the ship size and the sea state.

Passive Radar Array Processing with Non-Uniform Linear Arrays for Ground Target’s Detection and Localization

The problem of ground target detection with passive radars is considered. The design of an antenna array based on commercial elements is presented, based on a non-uniform linear array optimized according to sidelobe level requirements. Array processing techniques are applied in the cross-ambiguity function domain to exploit integration gain, system resolution and the sparsity of targets in this domain. A modified two-stage detection scheme is described, which is based on a previously-published one by other authors. All of these contributions are validated in a real semiurban scenario, proving the capabilities of detection, the direction of arrival estimation and the tracking of ground targets in the presence of big buildings that generate strong clutter returns. Detection performance is validated through the probability of false alarm and the probability of detection estimation with specified estimation errors.

A non-parametric CFAR detector based on SAR sea clutter statistical modeling

A fast non-parametric CFAR ship detector based on the statistical modeling of sea clutter in SAR images is proposed. Typical CFAR detectors, like the double parameter model (DPM), assume a Gaussian sea clutter model and usually degrade the image resolution using target windows. The proposed detector works in a pixel-by-pixel fashion, adaptively selecting a decision threshold for every single patch the SAR image is divided into. Sea clutter is modeled using the Generalized Gamma distribution. Therefore, the presence of different-sized ships will not become an issue and they will be better characterized, allowing the extraction of the target information, which could be used for refocusing, feature extraction, modeling and classification, while maintaining the resolution of the SAR image.

An adaptive threshold technique for the LR detector in K-clutter. Validation using IPIX radar

This paper tackles the detection of radar targets in presence of K-distributed clutter. A detection scheme composed by a preprocessing stage and a likelihood ratio test is proposed for adapting the detection threshold in order to maintain a constant false alarm probability. The conventional Cell-Averaging Constant False Alarm Rate (CA-CFAR) detector is considered as reference one. CA-CFAR technique uses output of the square law detector which cannot be the optimum one. The proposed detection scheme allows the use of a fixed threshold based on the finite number of clutter samples of the reference window. Considered detectors are evaluated with real data acquired by IPIX radar. Results prove that the designed adaptive threshold technique based on the likelihood ratio presents better detection capabilities than CA-CFAR, maintaining for both schemes a similar constant false alarm probability.

Passive radars as low environmental impact solutions for smart cities traffic monitoring

Passive radar systems are proposed as ground traffic control and monitoring tools in modern urban areas. These radars use communication networks transmitters available in the area of interest, instead of dedicated ones. The absence of a transmitter reduces dramatically the cost of development, deployment and maintenance, making feasible passive radar sensor networks for covering modern cities complex scenarios. They do not generate electromagnetic compatibility problems, and, more important, they are not associated with health issues. The illuminator selection is one of the most important tasks. The transmitted waveform, the transmitted power, the availability or the coverage areas are critical elements that are out of the control of the radar designer. The IDEPAR demonstrator is used in an urban environment to prove the traffic monitoring capabilities of passive radars exploiting Direct Video Broascasting -Terrestrial transmitters.

Doppler Processors as Suboptimum Approaches for Detecting Targets with Unknown Doppler Shift

This paper tackles the detection of Swerling I targets with unknown Doppler shift in colored Gaussian interference. If the radar illuminated area is enough large, a Gaussian clutter model can be assumed. The average likelihood ratio has been formulated and sub-optimum approaches based on the Constrained Generalized Likelihood Ratio (CGLR) analyzed. CGLR detectors have been considered as references solution for evaluating the detection performances and the computational cost of detection schemes including robust Doppler processor based on Moving Target Indicator (MTI) filters. The considered detection schemes are based on a Doppler filtering techniques to reduce the presence of clutter and an envelope detector. Two Doppler processor schemes have been considered: MTI filter designed for maximizing the improvement MTI factor averaged over all target Doppler shifts and a bank of MTI filters optimized for a set of target Doppler shifts. Results show that MTI filter-bank outperforms MTI averaged filter over a wider region of the Ωs variation interval reducing the computational cost with respect to the CGLR detector.

Segmentation Techniques for Land Mask Estimation in SAR Imagery

Synthetic Aperture Radar systems are powerful observation tools for maritime surveillance applications such as fisheries monitoring or pirates detection and oil slick detection. It is an important problem that has not been completely solved. In order to perform those detections, a search area is needed. In this paper, several segmentation techniques are proposed and compared to give rise to the search area. These techniques are applied to different SAR images acquired by TerraSAR-X. The best results in terms of land and sea classification are obtained when the superresolution algorithm combined with the wavelet transform based edge detector is applied.

Feasibility Study of EO SARs as Opportunity Illuminators in Passive Radars: PAZ-Based Case Study.

Passive radars exploit the signal transmitted by other systems, known as opportunity illuminators (OIs), instead of using their own transmitter. Due to its almost total invulnerability to natural disasters or physical attacks, satellite OIs are of special interest. In this line, a feasibility study of Earth Observation Synthetic Aperture Radar (EO SAR) systems as OIs is carried out taking into consideration signal waveform, availability, bistatic geometry, instrumented coverage area and incident power density. A case study based on the use of PAZ, the first Spanish EO SAR, is presented. PAZ transmitted waveform, operation modes, orbit characteristics and antenna and transmitter parameters are analyzed to estimate potential coverages and resolutions. The study concludes that, due to its working in on-demand operating mode, passive radars based on PAZ-type illuminators can be proposed as complementing tools during the sensor commissioning phase, for system maintenance and for improving its performance by providing additional information about the area of interest and/or increasing the data updating speed, exploiting other sensors during the time PAZ is not available.