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

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.

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

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.

The characterization and modeling of synthetic aperture sonar (SAS) image statistics are of importance for developing target‐on‐background detection and classification algorithms and for developing specialized filters for speckle noise reduction. A simple model is presented to predict the impact of amplitude scaling caused by seafloor ripples on SAS image speckle statistics. The continuous variation in scattering strength produced by ripples (i.e., ripple‐induced changes in seafloor slope) is treated as a deterministic amplitude scaling on image speckle produced by the SAS imaging process. Changes in image statistics caused by ripples are quantified in terms of an effective K‐distribution shape parameter. Agreement between shape parameter estimated from the scaling model and from SAS data collected in experiments off of Panama City, FL and off of the Ligurian coast of near La Spezia, Italy illustrates the efficacy of the model. [Work supported by ONR Grant Nos. N00014‐04‐1‐0013 and N00014‐06‐1‐0245.]

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.

Synthetic aperture sonar (SAS) imaging in a refracting environment requires that refraction be accounted for in the image processing to avoid blurring of the images. In deep water, e.g., this requires some knowledge of the refraction sound‐speed profile c(z). Ideally a ‘‘local’’ c(z) profile could be measured, but the measurement may only be valid for one platform position (not necessarily valid across the full synthetic aperture length) and at that one particular time. Historical data for c(z) may also be used for the region being imaged, but these data will not necessarily match the actual c(z) present during sonar data collection. This paper shows that SAS imagery in a refracting environment requires knowledge of the refraction for successful imaging, and that the refraction profile c(z) is a necessary part of matched‐filter image processing. For a deep water example where the exact c(z) profile is unknown, the use of historical c(z) data and reduced Doppler bandwidth, reduced‐resolution processing leads to useful sharp images that suffer only from incorrect bottom registration.

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.

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.

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.

Synthetic aperture sonar (SAS) provides high-resolution acoustic imaging by processing coherently the backscattered acoustic signal recorded over consecutive pings. Traditionally, object detection and classification tasks rely on high-resolution seafloor mapping achieved with widebeam, broadband SAS systems. However, aspect- or frequency-specific information is crucial for improving the performance of automatic target recognition algorithms. For example, low frequencies can be partly transmitted through objects or penetrate the seafloor providing information about internal structure and buried objects, while multiple views provide information about the object's shape and dimensions. Sub-band and limited-view processing, though, degrades the SAS resolution. In this paper, SAS imaging is formulated as an ℓ1-norm regularized least-squares optimization problem which improves the resolution by promoting a parsimonious representation of the data. The optimization problem is solved in a distributed and computationally efficient way with an algorithm based on the alternating direction method of multipliers. The resulting SAS image is the consensus outcome of collaborative filtering of the data from each ping. The potential of the proposed method for high-resolution, narrowband, and limited-aspect SAS imaging is demonstrated with simulated and experimental data.

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.

최근 고해상도의 해저면 영상을 취득하기 위한 합성개구 신호처리 및 수중 시스템 개발 연구가 여러 국가에서 활발히 진행 중에 있다. 국내에서도 합성개구소나의 필요성 및 중요성을 인식하여 기초적인 관련 연구가 시작되었으나 수신 배열 센서의 이동 속도에 의한 도플러 효과가 대부분 무시되어 왔다. 본 논문에서는 수신 배열 센서의 이동 속도에 따른 영향을 확인하기 위하여 공간 주파수 영역 보간법을 이용한 수동형 합성개구 신호처리 결과의 영상 왜곡 및 오차를 분석한다. 센서의 이동을 고려하지 않은 원래의 송신 신호만을 사용하는 경우 센서의 이동 속도가 증가함에 따라 음원의 위치 추정 오차가 증가하며 영상 왜곡이 현상이 뚜렷하게 나타난다. 센서의 이동을 고려하여 보정된 신호를 사용하는 경우 정확한 위치 추정이 가능하며 영상 왜곡 현상은 나타나지 않는다. 결론적으로 1 m/s 이상의 속도에서는 센서의 이동에 의한 도플러 현상을 보정하기 위한 알고리듬 적용이 필수적이다. In order to obtain high-resolution seafloor images, research on SA (Synthetic Aperture) processing and the development of related underwater systems have been performed in many countries. Recently the SA processing is also recognized as an important technique in Korea and researchers started related basic study. However, most previous studies ignored the Doppler effect by a moving receiver array. In this paper reconstructed SAS (Synthetic Aperture Sonar) images and position errors are analyzed according to the speed of a moving array for understanding its moving effect on the SAS images. In the analysis the spatial frequency domain interpolation algorithm is used. The results show that as the moving speed of the array increases the estimated position error also increases and image distortion gets worse when we do not consider the array motion. However, if the compensated receiver signals considering the array motion are used the position error and image distortion can be eliminated. In conclusion a signal processing scheme which compensates the Doppler effect is necessary especially in the condition where the array speed is over 1 m/s.

There is a growing demand for large-scale Synthetic Aperture Sonar (SAS) datasets. This demand stems from data-driven applications such as Automatic Target Recognition (ATR) [1]–[3], segmentation [4] and oceanographic research of the seafloor, simulation for sensor prototype development and calibration [5], and even potential higher level tasks such as motion estimation [6] and micronavigation [7]. Unfortunately, the acquisition of SAS data is bottlenecked by the costly deployment of SAS imaging systems, and even when data acquisition is possible, the data is often skewed towards containing barren seafloor rather than objects of interest. This skew introduces a data imbalance problem wherein a dataset can have as much as a 1000-to-1 ratio of seafloor background to object-of-interest SAS image chips.

The rich content of synthetic aperture sonar (SAS) data is typically used to generate imagery with resolution as high as theoretically possible. But when the ultimate purpose of the imagery is for detecting objects with sizes large compared to the resolution cell, exploiting the raw data in alternative ways can be more useful. We first show how multiple lower-resolution SAS images (i.e., “looks”) can be obtained in an efficient, principled manner by band-limiting the image wavenumber spectrum of a full-resolution SAS image. By combining these multiple looks (from different aspect and frequency bands), a despeckled image that enjoys greater (target) signal to (seabed) reverberation ratio can be produced. On a large set of real SAS data collected at sea, it is demonstrated how better target detection performance of underwater mines can be achieved by using the lower-resolution despeckled imagery rather than single-look, maximum-resolution imagery.

Efficient three-dimensional (3-d) convolutional neural networks (CNNs) are designed and trained for an underwater target classification task with volumetric synthetic aperture sonar (SAS) imagery. (The third dimension of the data represents depth into the sediment, thereby enabling the consideration of buried underwater objects.) The use of tiny networks containing relatively few parameters makes training with enormous input data volumes feasible even with modest computational power and limited computer memory. The promise of the approach is demonstrated for both buried and proud man-made objects present in real, measured SAS data cubes collected at aquatic sites by an experimental volumetric sonar system, called the Sediment Volume Search Sonar (SVSS). The classification performance of each 3-d CNN exhibits marked improvement over a prescreening detection algorithm alone, and the utility of an ensemble approach is also quantified. An analysis of the effective functionality of the learned networks is provided, with this also accompanied by figures showing example trained filters as well as intermediate representations of a data volume containing unexploded ordnance (UXO). The predictions of the 3-d CNN classifiers can provide valuable guidance for the efficient allocation of resources during real-world UXO remediation operations.