Target Detection Using a Wavelet-Based Fractal Scheme

In this thesis, a target detection technique using a rotational invariant wavelet-based scheme is presented. The technique is evaluated on Synthetic Aperture Rader (SAR) imaging and compared with a previously developed fractal-based technique, namely the extended fractal (EF) model. Both techniques attempt to exploit the textural characteristics of SAR imagery. Recently, a wavelet-based fractal feature set, similar to the proposed one, was compared with the EF feature for a general texture classification problem. The wavelet-based technique yielded a lower classification error than EF, which motivated the comparison between the two techniques presented in this paper. Experimental results show that the proposed techniques feature map provides a lower false alarm rate than the previously developed method

A location scale based CFAR detection framework for FOPEN SAR images

The problem of target detection in a complex clutter environment, with Constant False Alarm Ratio (CFAR), is addressed in this paper. In particular an algorithm for CFAR target detection is applied to the context of FOliage PENetrating (FOPEN) Synthetic Aperture Radar (SAR) imaging. The extreme value distributions family is used to model the data and exploiting the location-scale property of this family of distributions, a multi-model CFAR algorithm is derived. Performance analysis on real data confirms the capability of the developed framework to control the false alarm probability

Multi-model CFAR detection in FOliage PENetrating SAR images

A multi-model approach for Constant False Alarm Ratio (CFAR) detection of vehicles through foliage in FOliage PENetrating (FOPEN) SAR images is presented. Extreme value distributions and Location Scale properties are exploited to derive an adaptive CFAR approach that is able to cope with different forest densities. Performance analysis on real data is carried out to estimate the detection and false alarm probabilities in the presence of a ground truth

Constant False Alarm Rate Target Detection in Synthetic Aperture Radar Imagery

Target detection plays a significant role in many synthetic aperture radar (SAR) applications, ranging from surveillance of military tanks and enemy territories to crop monitoring in agricultural uses. Detection of targets faces two major problems namely, first, how to remotely acquire high resolution images of targets, second, how to efficiently extract information regarding features of clutter-embedded targets. The first problem is addressed by the use of high penetration radar like synthetic aperture radar. The second problem is tackled by efficient algorithms for accurate and fast detection. So far, there are many methods of target detection for SAR imagery available such as CFAR, generalized likelihood ratio test (GLRT) method, multiscale autoregressive method, wavelet transform based method etc. The CFAR method has been extensively used because of its attractive features like simple computation and fast detection of targets. The CFAR algorithm incorporates precise statistical description of background clutter which determines how accurately target detection is achieved. The primary goal of this project is to investigate the statistical distribution of SAR background clutter from homogeneous and heterogeneous ground areas and analyze suitability of statistical distributions mathematically modelled for SAR clutter. The threshold has to be accurately computed based on statistical distribution so as to efficiently distinguish target from SAR clutter. Several distributions such as lognormal, Weibull, K, KK, G0, generalized Gamma (GGD) distributions are considered for clutter amplitude modeling in SAR images. The CFAR detection algorithm based on appropriate background clutter distribution is applied to moving and stationary target acquisition and recognition (MSTAR) images. The experimental results show that, CFAR detector based on GGD outmatches CFAR detectors based on lognormal, Weibull, K, KK, G0 distributions in terms of accuracy and computation time.

Polarization techniques for mitigation of low grazing angle sea clutter

Maritime surveillance radars are critical in commerce, transportation, navigation, and defense. However, the sea environment is perhaps the most challenging of natural radar backdrops because maritime radars must contend with electromagnetic backscatter from the sea surface, or sea clutter. Sea clutter poses unique challenges in very low grazing angle geometries, where typical statistical assumptions regarding sea clutter backscatter do not hold. As a result, traditional constant false alarm rate (CFAR) detection schemes may yield a large number of false alarms while objects of interest may be challenging to detect. Solutions posed in the literature to date have been either computationally impractical or lacked robustness. This dissertation explores whether fully polarimetric radar offers a means of enhancing detection performance in low grazing angle sea clutter. To this end, MIT Lincoln Laboratory funded an experimental data collection using a fully polarimetric X-band radar assembled largely from commercial off-the-shelf components. The Point de Chene Dataset, collected on the Atlantic coast of Massachusetts’ Cape Ann in October 2015, comprises multiple sea states, bandwidths, and various objects of opportunity. The dataset also comprises three different polarimetric transmit schemes. In addition to discussing the radar, the dataset, and associated post-processing, this dissertation presents a derivation showing that an established multiple input, multiple output radar technique provides a novel means of simultaneous polarimetric scattering matrix measurement. A novel scheme for polarimetric radar calibration using a single active calibration target is also presented. Subsequent research leveraged this dataset to develop Polarimetric Co-location Layering (PCL), a practical algorithm for mitigation of low grazing angle sea clutter, which is the most significant contribution of this dissertation. PCL routinely achieves a significant reduction in the standard CFAR false alarm rate while maintaining detections on objects of interest. Moreover, PCL is elegant: It exploits fundamental characteristics of both sea clutter and object returns to determine which CFAR detections are due to sea clutter. We demonstrate that PCL is robust across a range of bandwidths, pulse repetition frequencies, and object types. Finally, we show that PCL integrates in parallel into the standard radar signal processing chain without incurring a computational time penalty

A region-based target detection method for SAR images (SAR görüntüleri için bölge tabanlı bir hedef tespit yöntemi)

Automatic target detection methods for synthetic aperture radar (SAR) images are sensitive to image resolution, size of the target to be detected, clutter complexity, and speckle noise level. A robust automatic target detection method needs to be less sensitive to the above factors. In this study, a constant false alarm rate (CFAR) based automatic target detection method which can find a target and its heterogeneous clutter independent of the image resolution and the target size has been developed. The proposed method provides efficient memory usage and low computational complexity

Sentetik açıklıklı radar görüntülerinde alan tabanlı hedef tespiti ve paralel gerçekleştirmesi (Region based target detection in synthetic aperture radar images and its parallel implementation)

Sentetik açıklıklı radar (SAR) görüntülerinde otomatik hedef tespiti yöntemleri görüntünün çözünürlüğüne, hedefin büyüklüğüne, parazit yankı karmaşıklığına ve benek gürültü seviyesine duyarlıdır. Gürbüz bir hedef tespiti yönteminin ise bu tür etkenlere daha az duyarlı olması istenir. Önerilen yöntem görüntünün öznitelik korumalı benek gürültü arındırma (feature preserving despeckling, FPD) yönteminden geçmiş hali üzerinden olası hedef bölgelerinin ve etrafındaki parazit yankı karmaşıklığının bulunması ve sabit yanlış alarm oranı elde edilecek şekilde eşiklenmesi esasına dayanmaktadır. Hesaplama verimliği OpenMP ve NVidia CUDA kullanılarak arttırılmış ve elde edilen hızlanmalar gösterilmiştir

A Novel Variable Index and Excision CFAR Based Ship Detection Method on SAR Imagery

When applying the constant false alarm rate (CFAR) detector to ship detection on synthetic aperture radar (SAR) imagery, multiple interferers such as upwelling, breaking waves, ambiguities, and neighboring ships in a dense traffic area will degrade the probability of detection. In this paper, we propose a novel variable index and excision CFAR (VIE-CFAR) based ship detection method to alleviate the masking effect of multiple interferers. Firstly, we improve the variable index (VI) CFAR with an excision procedure, which censors the multiple interferers from the reference cells. And then, the paper integrates the novel CFAR concept into a ship detection scheme on SAR imagery, which adopts the VIE-CFAR to screen reference cells and the distribution to derive detection threshold. Finally, we analyze the performances of the VIE-CFAR under different environments and validate the proposed method on both ENVISAT and TerraSAR-X SAR data. The results demonstrate that the proposed method outperforms other existing detectors, especially in the presence of multiple interferers

Robust CFAR Detector Based on Truncated Statistics for Polarimetric Synthetic Aperture Radar

Constant false alarm rate (CFAR) algorithms using a local training window are widely used for ship detection with synthetic aperture radar (SAR) imagery. However, when the density of the targets is high, such as in busy shipping lines and crowded harbors, the background statistics may be contaminated by the presence of nearby targets in the training window. Recently, a robust CFAR detector based on truncated statistics (TS) was proposed. However, the truncation of data in the format of polarimetric covariance matrices is much more complicated with respect to the truncation of intensity (single polarization) data. In this article, a polarimetric whitening filter TS CFAR (PWF-TS-CFAR) is proposed to estimate the background parameters accurately in the contaminated sea clutter for PolSAR imagery. The CFAR detector uses a polarimetric whitening filter (PWF) to turn the multidimensional problem to a 1-D case. It uses truncation to exclude possible statistically interfering outliers and uses TS to model the remaining background samples. The algorithm does not require prior knowledge of the interfering targets, and it is performed iteratively and adaptively to derive better estimates of the polarimetric covariance matrix (although this is computationally expensive). The PWF-TS-CFAR detector provides accurate background clutter modeling, a stable false alarm property, and improves the detection performance in high-target-density situations. RadarSat2 data are used to verify our derivations, and the results are in line with the theory