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.

A Comparison of Fixed Threshold CFAR and CNN Ship Detection Methods for S-band NovaSAR Images

NovaSAR is a commercial S-band Synthetic Aperture Radar (SAR) small satellite, built and operated by SSTL in the UK. One of its primary mission objectives is to carry out maritime surveillance and monitoring for security and defence applications. An investigation was carried out into comparing and contrasting conventional and new methods to perform automated ship detection in NovaSAR images. The outcome of this investigation could show the potential effectiveness of ship detection using spaceborne S-band SAR for Maritime Domain Awareness (MDA). The conventional approach is to apply a suitable distribution model to characterise sea surface clutter, followed by the implementation of a fixed threshold, Constant False Alarm Rate (CFAR) detection algorithm. In comparison, a RetinaNet-based convolutional neural network (CNN)solution was developed and trained on an open-source C-band dataset in order to determine the validity of applying non-native training data to S-band imagery. The detection performance was then compared with the CFAR technique, finding that for two selected test acquisitions a CNN-based ship detection algorithm was able to outperform a fixed threshold, CFAR-based method in the absence of native training data. CNN ship detection performance was further improved by applying transfer learning to a native S-band NovaSAR image dataset

Automatic and semi-automatic extraction of curvilinear features from SAR images

Extraction of curvilinear features from synthetic aperture radar (SAR) images is important for automatic recognition of various targets, such as fences, surrounding the buildings. The bright pixels which constitute curvilinear features in SAR images are usually disrupted and also degraded by high amount of speckle noise which makes extraction of such curvilinear features very difficult. In this paper an approach for the extraction of curvilinear features from SAR images is presented. The proposed approach is based on searching the curvilinear features as an optimum unidirectional path crossing over the vertices of the features determined after a despeckling operation. The proposed method can be used in a semi-automatic mode if the user supplies the starting vertex or in an automatic mode otherwise. In the semi-automatic mode, the proposed method produces reasonably accurate real-time solutions for SAR images

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

An Automatic Ship Detection Method Based on Local Gray-Level Gathering Characteristics in SAR Imagery

This paper proposes an automatic ship detection method based on gray-level gathering characteristics of synthetic aperture radar (SAR) imagery. The method does not require any prior knowledge about ships and background observation. It uses a novel local gray-level gathering degree (LGGD) to characterize the spatial intensity distribution of SAR image, and then an adaptive-like LGGD thresholding and filtering scheme to detect ship targets. Experiments on real SAR images with varying sea clutter backgrounds and multiple targets situation have been conducted. The performance analysis confirms that the proposed method works well in various circumstances with high detection rate, fast detection speed and perfect shape preservation

Ship Wake Detection in SAR Images via Sparse Regularization

In order to analyse synthetic aperture radar (SAR) images of the sea surface, ship wake detection is essential for extracting information on the wake generating vessels. One possibility is to assume a linear model for wakes, in which case detection approaches are based on transforms such as Radon and Hough. These express the bright (dark) lines as peak (trough) points in the transform domain. In this paper, ship wake detection is posed as an inverse problem, which the associated cost function including a sparsity enforcing penalty, i.e. the generalized minimax concave (GMC) function. Despite being a non-convex regularizer, the GMC penalty enforces the overall cost function to be convex. The proposed solution is based on a Bayesian formulation, whereby the point estimates are recovered using maximum a posteriori (MAP) estimation. To quantify the performance of the proposed method, various types of SAR images are used, corresponding to TerraSAR-X, COSMO-SkyMed, Sentinel-1, and ALOS2. The performance of various priors in solving the proposed inverse problem is first studied by investigating the GMC along with the L1, Lp, nuclear and total variation (TV) norms. We show that the GMC achieves the best results and we subsequently study the merits of the corresponding method in comparison to two state-of-the-art approaches for ship wake detection. The results show that our proposed technique offers the best performance by achieving 80% success rate.Comment: 18 page

Détection de bateaux dans les images de radar à ouverture synthétique

Le but principal de cette thèse est de développer des algorithmes efficaces et de concevoir un système pour la détection de bateaux dans les images Radar à Ouverture Synthetique (ROS.) Dans notre cas, la détection de bateaux implique en premier lieu la détection de cibles de points dans les images ROS. Ensuite, la détection d'un bateau proprement dit dépend des propriétés physiques du bateau lui-même, tel que sa taille, sa forme, sa structure, son orientation relative a la direction de regard du radar et les conditions générales de l'état de la mer. Notre stratégie est de détecter toutes les cibles de bateaux possibles dans les images de ROS, et ensuite de chercher autour de chaque candidat des évidences telle que les sillons. Les objectifs de notre recherche sont (1) d'améliorer 1'estimation des paramètres dans Ie modèle de distribution-K et de déterminer les conditions dans lesquelles un modèle alternatif (Ie Gamma, par exemple) devrait être utilise plutôt; (2) d'explorer Ie modèle PNN (Probabilistic Neural Network) comme une alternative aux modèles paramétriques actuellement utilises; (3) de concevoir un modèle de regroupement flou (FC : Fuzzy Clustering) capable de détecter les petites et grandes cibles de bateaux dans les images a un seul canal ou les images a multi-canaux; (4) de combiner la détection de sillons avec la détection de cibles de bateaux; (5) de concevoir un modèle de détection qui peut être utilisé aussi pour la détection des cibles de bateaux en zones costières.Abstract: The main purpose of this thesis is to develop efficient algorithms and design a system for ship detection from Synthetic Aperture Radar (SAR) imagery. Ship detection usually involves through detection of point targets on a radar clutter background.The detection of a ship depends on the physical properties of the ship itself, such as size, shape, and structure; its orientation relative to the radar look-direction; and the general condition of the sea state. Our strategy is to detect all possible ship targets in SAR images, and then search around each candidate for the wake as further evidence.The objectives of our research are (1) to improve estimation of the parameters in the K-distribution model and to determine the conditions in which an alternative model (Gamma, for example) should be used instead; (2) to explore a PNN (Probabilistic Neural Networks) model as an alternative to the commonly used parameteric models; (3) to design a FC (Fuzzy Clustering) model capable of detecting both small and large ship targets from single-channel images or multi-channel images; (4) to combine wake detection with ship target detection; (5) to design a detection model that can also be used to detect ship targets in coastal areas. We have developed algorithms for each of these objectives and integrated them into a system comprising six models.The system has been tested on a number of SAR images (SEASAT, ERS and RADARSAT-1, for example) and its performance has been assessed

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

Ship detection on open sea and coastal environment

Synthetic Aperture Radar (SAR) is a high-resolution ground-mapping technique with the ability to effectively synthesize a large radar antenna by processing the phase of a smaller radar antenna on a moving platform like an airplane or a satellite. SAR images, due to its properties, have been the focus of many applications such as land and sea monitoring, remote sensing, mapping of surfaces, weather forecasting, among many others. Their relevance is increasing on a daily basis, thus it’s crucial to apply the best suitable method or technique to each type of data collected. Several techniques have been published in the literature so far to enhance automatic ship detection using Synthetic Aperture Radar (SAR) images, like multilook imaging techniques, polarization techniques, Constant False Alarm Rate (CFAR) techniques, Amplitude Change Detection (ACD) techniques among many others. Depending on how the information is gathered and processed, each technique presents different performance and results. Nowadays there are several ongoing SAR missions, and the need to improve ship detection, oil-spills or any kind of sea activity is fundamental to preserve and promote navigation safety as well as constant and accurate monitoring of the surroundings, for example, detection of illegal fishing activities, pollution or drug trafficking. The main objective of this MSc dissertation is to study and implement a set of algorithms for automatic ship detection using SAR images from Sentinel-1 due to its characteristics as well as its ease access. The dissertation organization is as follows: Chapter 1 presents a brief introduction to the theme of this dissertation and its aim, as well as its structure; Chapter 2 summarizes a variety of fundamental key points from historical events and developments to the SAR theory, finishing with a summary of some well-known ship detection methods; Chapter 3 presents a basic guideline to choose the best ship detection technique depending on the data type and operational scenario; Chapter 4 focus on the CFAR technique detailing the implemented algorithms. This technique was selected, given the data set available for testing in this work; Chapter 5 presents the results obtained using the implemented algorithms; Chapter 6 presents the conclusions, final remarks and future work