The Mondrian Detection Algorithm for Sonar Imagery

The sequence of echoes detected by an active Synthetic Aperture Sonar (SAS) is coherently added in an appropriate way to produce an image with greatly enhanced resolution in the azimuth, or along-track direction when compared with an image obtained from a standard Side Looking Sonar (SLS). The SAS processing originates from the Synthetic Aperture Radar (SAR) concept. A complete introduction to SAR technique can be found in (Sherwin et al., 1962), (Walker, 1980), (Wiley, 1985) and (Curlander & McDonough, 1991). Raytheon was issued in 1969 a patent for a high-resolution seafloor imaging SAS (Walsh, 1969) and 1971 analyzed a potential system in terms of its resolution and signal-to-noise ratio. Cutrona was the first well-known radar specialist to point out how the various aspects of SAR could be translated to an underwater SAS (Cutrona, 1975). Hughes (1977) compared the performance of a standard SLS to an SAS and showed that the potential mapping rate for SAS was significantly higher than for side-looking sonar. At the time there was an assumption that the instability of the oceanic environment would prevent the formation of SAS imagery. Experimental work, which was performed by Williams (1976) and Christoff et al. (1982), refuted the instability worry. The verification of this assertion performed at higher frequencies by Gough & Hawkins (1989). Later, other concerns regarding the stability of the towed platform were also raised and some railor wire-guided trails where set up to avoid this extra complication. Nowadays there are a multiple of systems as hull mounted SAS systems, towed SAS systems and Autonomous Underwater Vehicles (AUV) systems. For further reading one can find an extended historical background of SAS in (Gough & Hawkins, 1997). Time and experience were needed to adapt SAR algorithms to SAS systems; the SAS systems use smaller radiating elements in proportion to the wavelength, which leads to higher radiation pattern of SAS with respect to SAR. The range migration effect on synthetic aperture processing is significant and pronounced in SAS imagery. An additional difference between SAR and SAS systems is the synthetic aperture time being greater in one order of magnitude in SAS, which leads to a phase corruption due to the medium fluctuations and platform instabilities. Typical synthetic aperture times for SAR are of the order of seconds with a medium coherence of some days, whereas for SAS the typical synthetic aperture time is of the order of several minutes with a similar medium coherence time (Marx et al. 2000). O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

Sidescan imaging sonar performance is often described using metrics such as resolution, maximum range, and area coverage rate. These attributes focus on describing how finely and over what area a sensor can make imagery of the seafloor and targets. While these metrics are important, they are inadequate for fully characterizing the quality of sonar imagery. Shadow regions often carry as much, and sometimes even more, information than the direct return from a target. The shadow contrast within an image is therefore a critical property, and it is the result of a complex interaction between the sonar hardware, the environment, and the signal processing. This paper builds on key results from the synthetic aperture radar and sonar literature to develop a comprehensive, quantitative model for predicting the shadow contrast ratio. A model for the contrast ratio is constructed using an approach that is similar to the development of the sonar equation. This ratio describes the average relative level between the seafloor and a shadow. The model includes the effects of the transducer and array design, ambient noise, quantization noise, unwanted volume and surface reverberation, and the signal processing used to construct the synthetic aperture sonar (SAS) imagery. The shadow contrast predicted by the model is compared to the contrast measured from SAS imagery, where close agreement is observed. Examples are presented where the model is applied to a hypothetical high-frequency imaging SAS sensor in a typical operating environment. The shadow contrast ratio is estimated as a function of the sensor range and key parameters of interest, such as receiver channel spacing, sediment backscatter coefficient, and the time-bandwidth product of the transmitted signal. At long ranges, the contrast ratio is limited by additive noise interference, while at near ranges the effect of the receiver channel spacing dominates the contrast ratio through along-track ambiguities. The use of this tool for analysis and prediction of image quality has significant implications for system design, mission planning, and data processing.

Characteristics of underwater targets displayed in synthetic aperture sonar (SAS) imagery vary depending on their environmental context. Discriminative features in sea grass may differ from the features that are discriminative in sand ripple, for example. Environmentally-adaptive target detection and classification systems that take into account environmental context, therefore, have the potential for improved results. This paper presents an end-to-end environmentally-adaptive target detection system for SAS imagery that performs target recognition while accounting for environmental context. First, locations of interest are identified in the imagery using the Reed-Xiaoli (RX) detector and a Non-Gaussian detector based on the multivariate Laplace distribution. Then, the Multiple Instance Learning via Embedded Instance Selection (MILES) approach is used to identify the environmental context of the targets. Finally, target features are extracted and a set of environmentally-specific k-Nearest Neighbors (k-NN) classifiers are applied. Experiments were conducted on a collection of both high and low frequency SAS imagery with a variety of environmental contexts and results show improved classification accuracy between target classes when compared with classification results with no environmental consideration.

Phase gradient autofocus (PGA) is widely used in spotlight SAR autofocus with strong robustness. However, being the different nature of synthetic aperture sonar (SAS) imagery, traditional PGA algorithm usually fails to yield satisfactory result on SAS image without modification. Three measures are taken to ensure that PGA can be applied in stripmap SAS autofocus. A SAS image is divided into sub-images which ensures that the phase error is invariant in each sub-image; The image shift in azimuth can be avoided by removing the mean value of the phase gradient and by using strong point targets in the image; The estimation accuracy can be improved by selecting range bins according to energy criteria combined with quality criteria. The proposed method is successfully applied on SAS image and excellent results are attained.

A trend in Mine Counter Measurement is towards Remotely Operated Vehicles or even Autonomous Underwater Vehicles (AUV) to increase the stand-off and reduce the risk for the personnel. The development of Synthetic Aperture Sonar (SAS) means that range independent high resolution images can be generated from an AUV. Hence the operator will be presented with a large amount of data to process. If the detection and classification process could be carried out automatically the workload would be reduced. Thus research into computer aided detection and computer aided classification (CAD/CAC) algorithms is an opportunity to improve the mine-hunting process. As indicated by the name CAD/CAC, the process is here considered as two consecutive steps. Beginning with an anomaly detection step where image pixels not belonging to the expected bottom reverberation pattern are identified. Anomaly pixels are then grouped into objects which are classified as a specific target. A detection method based on statistical properties and a classification method based on template matching are described. The methods are evaluated on SAS AUV data. The SAS AUV experiment was carried out in the Stockholm archipelago, partly in an area used by the Swedish navy for minehunting training. The seabed consists of soft postglacial clay and mud, a very suitable bottom for minehunting. After identifying six mine and mine-like objects the AUV was ordered to circulate around each target several times at different ranges. The purpose was to catch the angle dependence of the targets. The AUV was sailed at a constant depth and each object was illuminated from eight different horizontal angles repeated at three different ranges giving variation in elevation angle. Results are presented for the evaluation of the methods. Both detection based on statistics and template matching methods perform very well on this dataset. However, the used dataset has a high SNR and a high resemblance between training and testing data. Additional datasets are desirable including more difficult bottom characteristics. Furthermore, the used methods are applicable to side-scan sonar images.

In underwater synthetic aperture sonar (SAS) imagery, there is a need for accurate target recognition algorithms. Automated detection of underwater objects has many applications, not the least of which being the safe extraction of dangerous explosives. In this paper, we discuss experiments on a deep learning approach to binary classification of target and non-target SAS image tiles. Using a fused anomaly detector, the pixels in each SAS image have been narrowed down into regions of interest (ROIs), from which small target-sized tiles are extracted. This tile data set is created prior to the work done in this paper. Our objective is to carry out extensive tests on the classification accuracy of deep convolutional neural networks (CNNs) using location-based cross validation. Here we discuss the results of varying network architectures, hyperparameters, loss, and activation functions; in conjunction with an analysis of training and testing set configuration. It is also in our interest to analyze these unique network setups extensively, rather than comparing merely classification accuracy. The approach is tested on a collection of SAS imagery.

Synthetic aperture sonar (SAS) imagery can generate high resolution images of the seafloor. Thus, segmentation algorithms can be used to partition the images into different seafloor environments. In this paper, we compare two possibilistic segmentation approaches. Possibilistic approaches allow for the ability to detect novel or outlier environments as well as well known classes. The Possibilistic Fuzzy Local Information C-Means (PFLICM) algorithm has been previously applied to segment SAS imagery. Additionally, the Possibilistic K-Nearest Neighbors (PKNN) algorithm has been used in other domains such as landmine detection and hyperspectral imagery. In this paper, we compare the segmentation performance of a semi-supervised approach using PFLICM and a supervised method using Possibilistic K-NN. We include final segmentation results on multiple SAS images and a quantitative assessment of each algorithm.

Current sonar autofocus techniques for blur removal originate in the radar community but have not provided a complete solution for Synthetic Aperture Sonar (SAS) imagery. The wide-beam, wide-band nature of SAS imagery makes implementation of Synthetic Aperture Radar (SAR) autofocus techniques difficult. This paper describes a generalisation of the standard Phase Gradient Autofocus (PGA) algorithm used in spotlight SAR that allows operation with stripmap SAS geometries. PGA uses prominent points within the target scene to estimate image blurring and phase errors. We show how PGA can be generalised to work with wide-band, wide-beam stripmap geometries. The SPGA method works by employing wave number domain 2D phase estimation techniques. The 2D phase errors are related to aperture position errors using the wave number transform. Robust sway estimates are obtained by using redundancy over a number of target points. We also present an improved Phase Curvature Autofocus (PCA) algorithm using the wave number transform. Preliminary results from the two algorithms (both on field-collected and simulated data sets) are presented and related to those obtained using previous methods. A discussion of SPGA's benefits over traditional algorithms and the limitations of the SPGA algorithm is presented. The SPGA algorithm was found to perform better than 2-D PCA on both simulated and field-collected data sets. Further testing on a variety of target scenes and imagery is required to investigate avenues of autofocus improvement.

Synthetic aperture sonar (SAS) suffers from a fundamental problem: The navigation data accuracy required for coherent summation of the echo signals is not directly achievable. Unamended this issue leads to heavily blurred images. Fortunately, under certain conditions, algorithms like displaced phase center antenna (DPCA) can be used to fine tune the navigation from the raw echo data itself. In contrary to DPCA, strip-map phase gradient autofocus (SPGA) is an algorithm which extracts residual phase errors from processed synthetic aperture images. The results can be used to correct and reprocess the raw data yielding images with reduced blurring. In the presence of overlapping phase centers in raw data, as required for data driven micro navigation like DPCA, some assumptions of SPGA are violated and can cause degradation of it's results. In this paper we investigate the effect of overlapping (redundant) phase centers (RPC) in raw data on the performance of SPGA. Results indicate that the use of redundant data during image formation can bias the phase information in SAS imagery and therefore affects SPGA focusing. In this case, side lobe levels in the focused images are increased compared to images processed without RPCs. Suitable selection schemes should be employed prior to processing to avoid biasing of the phase information due to data collected at redundant phase centers if SPGA processing on top of DPCA is required.

Underwater object classification combining SAS and transferred optical-to-SAS Imagery

This paper describes a hybrid CPU/GPU implementation of the synthetic aperture sonar (SAS) processing algorithm developed under the Autonomous Naval Mine Counter Measures (ANMCM) programme at the NATO STO Centre for Maritime Research and Experimentation (CMRE). This new implementation allows processing of SAS data in real time onboard an autonomous underwater vehicle (AUV). This is a key component to enable the development of a higher level of autonomy, allowing the AUV to adapt the mission profile to environmental conditions and sonar performance in order to meet high level objectives. The software has been developed focusing on its embedded implementation on-board an AUV with a low-end NVIDIA GPU, but can also be used in post-processing taking advantage of a higher grade GPU. The first results obtained at sea demonstrated the capability to produce SAS images in real time.

In this chapter, the receding horizon Kalman filter is applied to underwater navigation systems. The ocean covers about two-thirds of the earth and has a great effect on human beings. However, the ocean is overlooked while we focus our attention on land and atmospheric issues; we have not been able to explore the full depths of the ocean, its abundant livings and non-living resources. For example, only recently we have discovered, by using manned submersibles, that a large amount of methane and carbon dioxide comes from the seafloor and extraordinary groups of organisms live in hydrothermal vent areas. However, a number of complex issues due to the unstructured and hazardous undersea environment make it difficult to survey in the ocean even though today’s technologies have allowed humans to land on the moon and robots to travel to Mars. Unmanned underwater vehicles (UUVs) can help us better understand marine and other environmental issues, protect the ocean resources of the earth from pollution, and efficiently utilize them for human welfare. The UUV is a platform for a variety of sensors: acoustic, magnetic, gravimetric and chemical ones. Most commercial UUVs are tethered and remotely operated, referred to as remotely operated vehicles (ROVs). Extensive use of manned submersibles and ROVs are currently limited to a few applications because of very high operational costs, operator fatigue and safety issues. The demand for advanced underwater vehicle technologies is growing and will eventually lead to fully autonomous and reliable underwater vehicles. Autonomous underwater vehicles (AUVs) were initially developed to perform missions that were not easy for ROVs and manned underwater vehicles. Since the autonomy allows AUVs to be used for risky missions such as a mine countermeasure (MCM) or under-ice operations, AUVs are replacing ROVs towed vehicles as well as manned underwater vehicles (Whitcomb, 2000). For detailed ocean surveys, an AUV acts as a more stable platform for precision sensors than ROVs or towed vehicles because an AUV is not subject to physical disturbances transmitted along the cable to the surface vessel. This absence of physical attachment also allows AUVs to measure ocean characteristics at specific depths and perform bottom-following missions as owing to its autonomy. In short, An AUV provides marine researchers with a new form of access to deeper ocean. For an AUV to successfully complete a typical survey mission, it must follow a path specified by the operator as closely as possible and arrive at a precise location for collecting data. When an AUV is not able to follow the path accurately during the mission, critical Source: Kalman Filter, Book edited by: Vedran Kordic, ISBN 978-953-307-094-0, pp. 390, May 2010, INTECH, Croatia, downloaded from SCIYO.COM

The performance of an automatic target recognition (ATR) system in the context of naval mine detection is severely affected by the underwater environment. Especially in regions with the presence of sand ripples or mine-sized stones the number of false alarms can become unacceptable high, if the detection algorithm does not account for the type of seafloor. Therefore, a robust way of discriminating between the three most important types of seafloor in the context of mine countermeasures (MCM), flat bottom, rocky bottom and sand ripples, is needed. In this paper five handcrafted features for seafloor classification from the literature are analysed and compared to the performance of a convolutional neural network (CNN) that learns the relevant features from the training data. The evaluation data was collected with an autonomous underwater vehicle (AUV) equipped with an synthetic aperture sonar. Experiments showed that the CNN approach outperformed a support vector machines classifier with the handcrafted features, albeit slightly, with a classification rate of 98.7% over 9420 examples.

This paper introduces a new subspace-based detection method for multichannel (high frequency and broadband) synthetic aperture sonar (SAS) imagery. An image-dependent dictionary learning method is applied to form the appropriate dictionary matrices for representing target and nontarget image snippets. The hypothesis testing is done by forming a test statistic that relies on the residual error power ratio in representing an unknown image snippet using the target and nontarget dictionary matrices. To avoid the computational bottleneck in most dictionary learning methods, a new recursive method is introduced which does not require any matrix inversion or singular value decomposition (SVD). The proposed detection method was then implemented and benchmarked against a matched subspace detection method for detecting mine-like objects. Results are then presented on two sonar imagery data sets collected in two geographically disparate locations.

Autonomous underwater vehicle (AUV) navigation and localization is crucial and challenging in Global Positioning System-denied environments. AUV carrying synthetic aperture sonars (SASs) can provide precise motion estimation of the platforms. This study focuses on enhancing the performance of AUV localization using dual-sided SASs. The two-dimensional velocities of AUV are estimated using dual-sided SAS echoes. An extended Kalman filter (EKF) is then adopted for dead reckoning using the estimated velocities. The experimental results demonstrate that the proposed method has better localization accuracy than that of the Doppler velocity log (DVL).