Quantifying and Modeling the Effects of Internal Waves on Synthetic Aperture Sonar

Synthetic aperture sonar (SAS) is based on synthetic aperture radar, with a number of key factors increasing the complexity of data collection. One of the assumptions made with respect to SAS image reconstruction is the presence of a constant sound speed. As a nearfield imaging system, SAS is sensitive to the breaking of this assumption. The sound speed in the ocean varies with depth. Variations in sound speed can come in the form of internal waves. Internal waves propagating up the slope of the continental shelf are subject to breaking mechanisms that result in the propagation of boluses shoreward. Internal wave boluses are three dimensional features consisting of colder, higher density water. Since the internal wave boluses are composed of colder seawater, the speed of sound is different than in the surrounding environment. The change in sound speed changes the timing and phase of propagating acoustic rays causing degradation in SAS image quality. Not only do the internal waves violate the constant sound speed assumption made by SAS for image formation, but they also influence the travel of acoustic rays due to a geometric lensing effect. The lensing effect causes large refractive effects near the top of the bolus, resulting in a bright region and shadow region within the image. The goal of this study was to quantify the effects of internal waves on SAS image resolution and subsequently model these effects. The quantification of the effects was performed utilizing point targets within the SAS image. The point spread function of the point targets was estimated and used as a proxy for the image resolution and showed that internal waves can cause resolution loss on the order of two to four times than in the absence of a bolus or sound speed error. A numerical ray tracing model was used to estimate the resolution loss in SAS imagery in the presence of internal waves. An analytical model derived in order to better characterize the impacts of internal waves on SAS resolution. Beamforming was also performed over simulated imagery in the presence and absence of internal waves. The models agreed well with each other and the observed resolution loss in collected SAS data. Based on the success of modeling attempts, it is reasonable to develop a method for full inversion for bolus parameters. Given the agreement of the models with data it may be possible to develop methods to compensate for timing errors caused by the presence of internal waves and return the ideal image resolution

Iterative, Deep Synthetic Aperture Sonar Image Segmentation

Synthetic aperture sonar (SAS) systems produce high-resolution images of the seabed environment. Moreover, deep learning has demonstrated superior ability in finding robust features for automating imagery analysis. However, the success of deep learning is conditioned on having lots of labeled training data, but obtaining generous pixel-level annotations of SAS imagery is often practically infeasible. This challenge has thus far limited the adoption of deep learning methods for SAS segmentation. Algorithms exist to segment SAS imagery in an unsupervised manner, but they lack the benefit of state-of-the-art learning methods and the results present significant room for improvement. In view of the above, we propose a new iterative algorithm for unsupervised SAS image segmentation combining superpixel formation, deep learning, and traditional clustering methods. We call our method Iterative Deep Unsupervised Segmentation (IDUS). IDUS is an unsupervised learning framework that can be divided into four main steps: 1) A deep network estimates class assignments. 2) Low-level image features from the deep network are clustered into superpixels. 3) Superpixels are clustered into class assignments (which we call pseudo-labels) using $k$-means. 4) Resulting pseudo-labels are used for loss backpropagation of the deep network prediction. These four steps are performed iteratively until convergence. A comparison of IDUS to current state-of-the-art methods on a realistic benchmark dataset for SAS image segmentation demonstrates the benefits of our proposal even as the IDUS incurs a much lower computational burden during inference (actual labeling of a test image). Finally, we also develop a semi-supervised (SS) extension of IDUS called IDSS and demonstrate experimentally that it can further enhance performance while outperforming supervised alternatives that exploit the same labeled training imagery.Comment: arXiv admin note: text overlap with arXiv:2107.1456

Synthetic Aperture Anomaly Imaging

Previous research has shown that in the presence of foliage occlusion, anomaly detection performs significantly better in integral images resulting from synthetic aperture imaging compared to applying it to conventional aerial images. In this article, we hypothesize and demonstrate that integrating detected anomalies is even more effective than detecting anomalies in integrals. This results in enhanced occlusion removal, outlier suppression, and higher chances of visually as well as computationally detecting targets that are otherwise occluded. Our hypothesis was validated through both: simulations and field experiments. We also present a real-time application that makes our findings practically available for blue-light organizations and others using commercial drone platforms. It is designed to address use-cases that suffer from strong occlusion caused by vegetation, such as search and rescue, wildlife observation, early wildfire detection, and sur-veillance

Automatic refocus and feature extraction of single-look complex SAR signatures of vessels

In recent years, spaceborne synthetic aperture radar ( SAR) technology has been considered as a complement to cooperative vessel surveillance systems thanks to its imaging capabilities. In this paper, a processing chain is presented to explore the potential of using basic stripmap single-look complex ( SLC) SAR images of vessels for the automatic extraction of their dimensions and heading. Local autofocus is applied to the vessels' SAR signatures to compensate blurring artefacts in the azimuth direction, improving both their image quality and their estimated dimensions. For the heading, the orientation ambiguities of the vessels' SAR signatures are solved using the direction of their ground-range velocity from the analysis of their Doppler spectra. Preliminary results are provided using five images of vessels from SLC RADARSAT-2 stripmap images. These results have shown good agreement with their respective ground-truth data from Automatic Identification System ( AIS) records at the time of the acquisitions.Postprint (published version

Multi-algorithm Swath Consistency Detection for Multibeam Echosounder Data

It is unrealistic to expect that any single algorithm for pre-filtering Multibeam Echosounder data will be able to detect all of the “noise in such data all of the time. This paper therefore presents a scheme for fusing the results of many pre-filtering sub-algorithms in order to form one, significantly more robust, meta-algorithm. This principle is illustrated on the problem of consistency detection in regions of sloping bathymetry. We show that the meta-algorithm is more robust, adapts dynamically to sub-algorithm performance, and is consistent with operator assessment of the data. The meta-algorithm is called the Multi-Algorithm Swath Consistency Detector

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