This paper presents the findings related to the design solution options for a next-generation C-band Synthetic Aperture Radar (SAR) mission, developed to address the Harmonized User Needs (HUN) in Earth observation (EO) data as defined by several departments of the Government of Canada. The work analyses various mission solution options, including multi-satellite constellations, and their performance to evaluate feasibility and assess their compliance with the HUN as well as minimize the associated lifecycle costs, technical risks, implementation schedule, and programmatic challenges. This mission concept contributes to the advancement of space-based surveillance solutions aligned with Canada’s long-term strategic objectives to ensure service continuity for Earth Observation and national security applications. Systematic user needs analysis helped to reveal the importance of high-resolution (1–5 m), enhanced interferometric, polarimetric SAR interferometry (PolInSAR) and other capabilities. Two satellite constellation configurations are proposed: (1) a three-medium-satellite setup with a tandem pair, and (2) a five-large-satellite system incorporating tandem and optimal orbits. Employing High-Resolution Wide Swath (HRWS) imaging modes and full polarimetric capability. Performance simulations indicate low Noise Equivalent Sigma Zero (NESZ) with wide swath width fully addresses driving needs for sea ice and ocean monitoring, covering most of the Canadian areas of interest, with the revisit time of less than 4–6 hours. Orbit optimization ensures high revisit rates, enabling novel interferometric SAR (InSAR) capabilities with observations separated by only a few hours. This mission concept, considering two options with three medium and with five large satellites, respectively, offers a flexible, scalable, and strategically impactful solution for Earth Observation (EO) service continuity and technological leadership for Canada until 2050 and beyond.

The detection of concealed explosives is an active area of concern for defence and security forces. Radar technology, with the ability to penetrate barriers and detect the motion of vibrating objects, provides an attractive method for detecting concealed threats and, thereby, protecting assets and lives. A system has been developed to detect concealed threats by inducing acoustically driven vibrations using a loudspeaker and measuring the resulting micro-Doppler signal. The prototype, known as the Micro-Doppler Signatures Indicating eXplosives (MiDSIX) system, is evaluated against replica improvised explosive devices (IEDs) concealed behind a variety of barriers that are constructed from common materials. The characterization of the induced micro-Doppler signal and the detection of concealed IEDs explosives are demonstrated in multiple settings. The MiDSIX system offers a new method for detecting concealed explosives that can be easily deployed with a flexible operation.

Salient object detection (SOD) is the operation of detecting and segmenting a salient object in a natural scene. Several studies have examined various state-of-the-art machine learning approaches for SOD. In particular, deep convolutional neural networks (CNNs) are commonly applied for SOD because of their powerful feature extraction abilities. In this paper, we investigate the semantic segmentation capability of several well-known pre-trained models, including FCNs, VGGs, ResNets, MobileNet-v2, Xception and InceptionResNet-v2. These models have been trained over an ImageNet dataset, fine-tuned on a MSRA-10K dataset and evaluated using other public datasets, such as ECSSD, MSRA-B, DUTS and THUR15k. The results illustrate the superiority of ResNet50 and ResNet18, which have mean absolute errors (MAE) of approximately 0.93 and 0.92, respectively, compared to other well-known FCN models. Moreover, the most robust model against noise is ResNet50, whereas VGG-16 is the most sensitive, relative to other state-of-the-art models.

This article investigates and compares polarimetric signatures of icebergs embedded in sea ice and icebergs in open water. The main objective is to study on the backscatter properties of melting iceberg and to check on whether there is any distinguishable property in them in the case of different background clutter conditions (i.e. sea ice and open water). This study results will improve the potential of iceberg detection using radar polarimetry. RADARSAT-2 images have been used for the analysis acquired over locations near the coastline (approximately 3–35 km) of the island of Newfoundland. For analysis, polarimetry parameters, such as co-(HH) and cross-(HV) polarization and several popular decomposition techniques, specifically Pauli, Freeman–Durden, Yamaguchi, Cloud–Pottier, and van Zyl, have been used to determine the polarimetric signatures of icebergs and sea ice. The statistical hypothesis T-test has been applied to achieve a precise comparison among backscatters from different icebergs groups. Statistical results tend to show a dominant surface scattering mechanism for icebergs in all types of clutter conditions. Moreover, icebergs in open water produce larger volume scatter than icebergs in sea ice, whereas icebergs in sea ice produce larger surface scatter than icebergs in open water.

Human Visual System (HVS) has the ability to focus on specific parts of the scene, rather than the whole scene. This phenomenon is one of the most active research topics in the computer vision and neuroscience fields. Recently, deep learning models have been used for visual saliency prediction. In this paper, we investigate the performance of five state-of-the-art deep neural networks (VGG-16, ResNet-50, Xception, InceptionResNet-v2, and MobileNet-v2) for the task of visual saliency prediction. In this paper, we train five deep learning models over the SALICON dataset and then use the trained models to predict visual saliency maps using four standard datasets, namely: TORONTO, MIT300, MIT1003, and DUT-OMRON. The results indicate that the ResNet-50 model outperforms the other four and provides a visual saliency map that is very close to human performance.

A human Visual System (HVS) has the ability to pay visual attention, which is one of the many functions of the HVS. Despite the many advancements being made in visual saliency prediction, there continues to be room for improvement. Deep learning has recently been used to deal with this task. This study proposes a novel deep learning model based on a Fully Convolutional Network (FCN) architecture. The proposed model is trained in an end-to-end style and designed to predict visual saliency. The entire proposed model is fully training style from scratch to extract distinguishing features. The proposed model is evaluated using several benchmark datasets, such as MIT300, MIT1003, TORONTO, and DUT-OMRON. The quantitative and qualitative experiment analyses demonstrate that the proposed model achieves superior performance for predicting visual saliency.

This letter presents the validation of an electromagnetic (EM) backscatter model of icebergs at C-band by comparing the performances of target classifiers trained with both modeled and real synthetic aperture radar (SAR) data. Simulated SAR data were obtained in a combination of imaging beam modes and scene parameters to produce 216 simulated Sentinel-1 C-band SAR images. Parameters consisted of Sentinel-1 IW1 (33.1°) and IW3 (43.1°) beam modes with varying wind speed (5 and 10 m/s), wind direction (0°, 45°, and 90°), and target orientation (0°, 45°, and 90°). Simulations were created from an EM SAR simulator called GRECOSAR, which took 3-D profiles of iceberg and ship targets and parameters necessary to closely mimic the real scenes. 3-D models of three icebergs were captured in a field study off the coast of Bonavista, Newfoundland, and Labrador, Canada in June 2017. Three generic ship models were sourced from an online inventory and scaled to a size equivalent to that of the iceberg targets. Real SAR image data were drawn from in-house data set collected from a complementary research program. Classifiers including support vector machine (SVM), Random Forest (RanFor), k-nearest neighbor (kNN), and neural network (NN) were trained with targets from modeled SAR data and then gradually mixed with real SAR data. Target classifier performance from the modeled target data was shown to be similar to classifiers trained entirely from real SAR data. The similarity in accuracy provides an indication of the validity of the modeled SAR data for this specific application.

This article presents an electromagnetic backscatter model of iceberg and compares the modeled scattering behavior with C-band RADARSAT-2 synthetic aperture radar (SAR) images. It also explores iceberg SAR signature variability over various ocean parameters. Three-dimensional (3-D) profiles of icebergs were captured in a field study off the coast of Bonavista, NL, Canada, in June 2017 at the time of an SAR satellite overpass. The 3-D profiles were captured from a vessel, using a LiDAR and multibeam sonar. The SAR image and 3-D profiles were captured within hours of one another. Simulated SAR images of the icebergs were generated in a simulator called GRECOSAR with the satellite, target orientation, and ocean parameters that closely mimic the real SAR scene. A new ocean model was introduced to model an ocean backscatter at satellites' lower incidence angle beam mode. Comparison between real and simulated SAR images of the icebergs shows good agreement in terms of SAR signature, total radar cross section, and polarimetric decomposition. Wind direction was varied over 90° extent to observe icebergs' backscatter variability in the simulator. Furthermore, simulated SAR images were generated for low and high wind conditions. Our study finds that the macrostructure of the melt iceberg dominates its polarimetric behavior of its backscatter. Large variability of iceberg SAR signature over varying ocean parameters was also observed. A mathematical model that considers the melting condition of iceberg suggested that significant backscattering can reflect from top surface when the melt water layer could be as little as 0.1 mm.

Human eye movement is one of the most important functions for understanding our surroundings. When a human eye processes a scene, it quickly focuses on dominant parts of the scene, commonly known as a visual saliency detection or visual attention prediction. Recently, neural networks have been used to predict visual saliency. This paper proposes a deep learning encoder-decoder architecture, based on a transfer learning technique, to predict visual saliency. In the proposed model, visual features are extracted through convolutional layers from raw images to predict visual saliency. In addition, the proposed model uses the VGG-16 network for semantic segmentation, which uses a pixel classification layer to predict the categorical label for every pixel in an input image. The proposed model is applied to several datasets, including TORONTO, MIT300, MIT1003, and DUT-OMRON, to illustrate its efficiency. The results of the proposed model are quantitatively and qualitatively compared to classic and state-of-the-art deep learning models. Using the proposed deep learning model, a global accuracy of up to 96.22% is achieved for the prediction of visual saliency.

A multi-category numerical sea ice model CICE was used along with data assimilation to derive sea ice parameters in the region of Baffin Bay and Labrador Sea. The assimilation of ice concentration was performed using the data derived from the Advanced Microwave Scanning Radiometer (AMSR-E and AMSR2). The model uses a mixed-layer slab ocean parameterization to compute the sea surface temperature (SST) and thereby to compute the freezing and melting potential of ice. The data from Advanced Very High Resolution Radiometer (AVHRR-only optimum interpolation analysis) were used to assimilate SST. The modelled ice parameters including concentration, ice thickness, freeboard and keel depth were compared with parameters estimated from remote-sensing data. The ice thickness estimated from the model was compared with the measurements derived from Soil Moisture Ocean Salinity – Microwave Imaging Radiometer using Aperture Synthesis (SMOS–MIRAS). The model freeboard estimates were compared with the freeboard measurements derived from CryoSat2. The ice concentration, thickness and freeboard estimates from the model assimilated with both ice concentration and SST were found to be within the uncertainty in the observation except during March. The model-estimated draft was compared with the measurements from an upward-looking sonar (ULS) deployed in the Labrador Sea (near Makkovik Bank). The difference between modelled draft and ULS measurements estimated from the model was found to be within 10 cm. The keel depth measurements from the ULS instruments were compared to the estimates from the model to retrieve a relationship between the ridge height and keel depth.

Use of machine learning to develop algorithms for distinguishing iceberg and vessel targets requires large validated data sets that are often costly, time consuming and, in some cases, inaccessible. Generating electromagnetic (EM) backscatter models of iceberg and ship targets can be a vital step in developing a robust iceberg/ship classification algorithm. In this work, EM backscatter models for icebergs are developed using an EM backscatter modelling tool called GRECOSAR and compared with ground truth data. The imaging scene consists of iceberg targets surrounded by the ocean surface. The 3D computer aided design models of the icebergs were obtained using LiDAR and multi-beam sonar data collected during a field program off the coast of Salvage, Newfoundland and Labrador, Canada. While profiling the iceberg targets, a synthetic aperture radar (SAR) image from Sentinel-1A was captured and compared with the simulated SAR images. Comparisons made in terms of total radar cross section (TRCS) and the SAR signature of the targets generally indicate credible simulations. Simulated SAR images were generated at low and high dielectric conditions to mimic cold and melt iceberg surfaces. Variability of the TRCS and morphology as a function of target orientation highlights the usefulness of EM modelling in developing robust iceberg/ship classifiers.

Safe offshore operations and navigation in Arctic water requires reliable surveillance of icebergs. Synthetic Aperture Radar (SAR) is widely used by governments and industry to monitor iceberg locations in ice frequented waters. One of the primary challenges in using SAR data for this application is the discrimination of vessel targets from icebergs. In the context of iceberg surveillance, vessel targets are considered false alarms and the same is true of icebergs in vessel monitoring applications. The present state of the art in SAR-based vessel and iceberg discrimination is the use of machine learning to train algorithms to distinguish between vessel and iceberg backscatter. Machine learning requires the use of large datasets of validated iceberg and vessel targets, which can be costly and time consuming to collect. One solution to the problem is the use of a physical-based electromagnetic (EM) backscatter modelling of iceberg and ship targets in SAR data. The availability of such a model could prove to be an important step in developing a robust iceberg/ship classification algorithm. An EM backscatter model of various iceberg and ship targets has been developed by the authors and is presented and compared with ground truth data. To perform the EM modelling, a satellite EM backscatter modelling tool named GRECOSAR is used with the necessary geometrical, satellite, and orbital parameters; the output of the model is a simulated satellite SAR image chip of the modelled scene, usually a target on an ocean background. The tool is capable of producing images of complex scenes with a large variety of satellite parameters and with a full polarimetric capability. The functionality of the software has been rigorously tested and verified by comparing radar backscatter generated from standard canonical objects (sphere, dihedral and trihedral) with their theoretical values. In this present study, the simulation scene consisted of an iceberg model surrounded by an ocean surface. The 3D CAD models of actual icebergs were obtained using LiDAR and multi-beam sonar in a field program off the coast of Salvage, Newfoundland and Labrador, Canada. The surrounding ocean surface is a flat surface 2D CAD model whose height is modulated by an embedded multi harmonic sea model. The parameters of the multi harmonic sea model are set by selecting pre-defined sea states in the software according to Pierson-Moskowitz spectrum. As a first step, common characteristics for a small ocean patch have been examined and verified in the simulated images. RADARSAT-2 and Sentinel-1 satellite parameters were used for simulations at varying incidence angels (20°45°), at sea states-0 and 2 and in several Fine Quad beam modes for RADARSAT and IWS modes for Sentinel-1. An ocean chip size of 150x150 m was modelled with several iceberg models embedded at the middle of the scene. A facet size of 5 cm was used while meshing the scene as a compromise between computational load and accuracy. The dielectric permittivity of sea water was set to at 62.5-j10.3 assuming a water temperature of 0° C, a salinity of 35 ppt and a frequency of 5.4 GHz. The dielectric permittivity of iceberg was varied in the models to and compared with satellite SAR imagery acquired over the icebergs near the time of the field program. This iterative variation was necessary to account for the lack of a volume backscatter component from the GRECOSAR models; GRECOSAR is presently only able to produce the surface scattering of the target backscatter. The implications of this are presently being investigated by the analysis of coherent target decompositions of RADARSAT-2 Fine Quad imagery of icebergs. The results of all of the analysis conducted to date will be included in the presentation.

The assessment of the blood volume is crucial for the management of many acute and chronic diseases. Recent studies have shown that circulating blood volume correlates with the cross-sectional area (CSA) of the internal jugular vein (IJV) estimated from ultrasound imagery. In this paper, a semi-automatic segmentation algorithm is proposed using a combination of region growing and active contour techniques to provide fast and accurate segmentation of IJV ultrasound videos. The algorithm is applied to track and segment the IJV across a range of image qualities, shapes and temporal variation. The experimental results show that the algorithm performs well compared to expert manual segmentation and outperforms several published algorithms incorporating speckle tracking.

Traditional methods of capturing vital signs by monitoring electrical impulses are quite effective however this data has the potential to be extracted from alternative technology. Non-invasive monitoring using low-cost ultrasound imaging of arterial and venous vasculature has the potential to detect standard vital signs such as heart and respiratory rate as well as additional parameters such as relative changes in circulating blood volume. This paper explores the feasibility of using ultrasound to monitor these signals by detecting spatial and temporal changes in the internal jugular vein (IJV). Ultrasound videos of the jugular in the transverse plane were collected from a subset of healthy subjects. Frame-by-frame segmentation of the IJV demonstrates frequency characteristics similar to certain physiological systems. Heart and respiratory rate appear to be present in IJV cross-sectional area variations in select ultrasound clips and may provide information regarding the severity of a patient’s illness.

Portable ultrasound is commonly used to image blood vessels such as the Inferior Vena Cava or Internal Jugular Vein (IJV) in the attempt to estimate patient intravascular volume status. A large number of features can be extracted from a vessel’s cross section. This paper examines the role of shape factors and statistical moment descriptors to classify healthy subjects enrolled in a simulation modeling relative changes in volume status. Features were evaluated using a range of selection methods and tested with a variety of classifiers. It was determined that a subset of features derived from moments are the most appropriate for this task.

Abstract The work focuses on retrieving sea ice parameters using reanalysis, climatological and remote sensing data. A numerical sea ice model was implemented with a data assimilation scheme on a high performance computer. The model input includes atmospheric reanalysis and ocean climatological data. The assimilation of data acquired from satellite microwave radiometer improves model accuracy. The advantage of the model is the possibility to forecast ice parameters such as concentration, thickness, draft, ridging etc. on a high resolution scale. The modeled ice parameters can be used for risk analysis for offshore infrastructure and ship navigation in the ice covered regions. The results can also be used in regional climate studies by coupling with ocean-atmospheric models. The model was extensively tested and evaluated with satellite data and field measurements. The simulated ice draft results demonstrated a good agreement with the measurements from upward looking sonar (ULS) deployed on the Makkovik Bank (in the Labrador Sea). For example, the standard deviation (STD) of level ice draft is less than 5.0 cm and the bias is less than 0.2 cm for March-April of 2009. The simulated ice thickness was also compared with the thickness derived from Soil Moisture Ocean Salinity - Microwave Imaging Radiometer using Aperture Synthesis (SMOS-MIRAS) (). The results show that the estimated thickness from the model is within the uncertainty limits of the SMOS product.

This paper presents a simulation study of an autonomous underwater vehicle AUV navigation system operating in a GPS-denied environment. The AUV navigation method makes use of underwater transponder positioning and requires only one transponder. A multirate unscented Kalman filter is used to determine the AUV orientation and position by fusing high-rate sensor data and low-rate information. The paper also proposes a gradient-based, efficient, and adaptive novel algorithm for plume boundary tracking missions. The algorithm follows a centralized approach and it includes path optimization features based on gradient information. The proposed algorithm is implemented in simulation on the AUV-based navigation system and successful boundary tracking results are obtained.

Reductionism and holism are two worldviews underlying the fields of linear and nonlinear signal processing, respectively. Conventional radar resolution theory is motivated by the former view, and it is violated by nonlinear phase modulation induced by dispersive scattering typically associated with extended targets. Motivated by the latter view, this paper offers a new insight into the process of feature extraction for target-recognition applications in single-channel imagery output from synthetic aperture radar processors. Two novel frameworks for holism-based feature extraction are presented. The first framework is based solely on the often-ignored phase chip. The second framework uses the complex-valued 2-D synthetic aperture radar chip after it is transformed into a 1-D vector. Representative features are introduced under each framework. Further, for comparison purposes, baseline features from the power-detected chip are also considered. Three feature sets are extracted from the real-world MSTAR data set and used separately and combinatorially to design multiple instances of an eight-class support vector machine classifier. A classification accuracy of 93.42% is achieved for the holism-based features. This is in comparison to 73.63% for the baseline features. Using Fisher scoring to measure the information contained in each feature, top-ranked features from the first and second holism-based frameworks, respectively, are found to be 7 and 160 times those of the baseline features. Because the nonlinear phenomenon is resolution dependent, our proposed approach is expected to achieve even greater accuracy for synthetic aperture radar sensors with higher resolution.

The purpose of this paper is to survey and assess the state-of-the-art in automatic target recognition for synthetic aperture radar imagery (SAR-ATR). The aim is not to develop an exhaustive survey of the voluminous literature, but rather to capture in one place the various approaches for implementing the SAR-ATR system. This paper is meant to be as self-contained as possible, and it approaches the SAR-ATR problem from a holistic end-to-end perspective. A brief overview for the breadth of the SAR-ATR challenges is conducted. This is couched in terms of a single-channel SAR, and it is extendable to multi-channel SAR systems. Stages pertinent to the basic SAR-ATR system structure are defined, and the motivations of the requirements and constraints on the system constituents are addressed. For each stage in the SAR-ATR processing chain, a taxonomization methodology for surveying the numerous methods published in the open literature is proposed. Carefully selected works from the literature are presented under the taxa proposed. Novel comparisons, discussions, and comments are pinpointed throughout this paper. A two-fold benchmarking scheme for evaluating existing SAR-ATR systems and motivating new system designs is proposed. The scheme is applied to the works surveyed in this paper. Finally, a discussion is presented in which various interrelated issues, such as standard operating conditions, extended operating conditions, and target-model design, are addressed. This paper is a contribution toward fulfilling an objective of end-to-end SAR-ATR system design.

The purpose of this paper is to survey and assess the state-of-the-art in automatic target recognition for synthetic aperture radar imagery (SAR-ATR). The aim is not to develop an exhaustive survey of the voluminous literature, but rather to capture in one place the various approaches for implementing the SAR-ATR system. This paper is meant to be as self-contained as possible, and it approaches the SAR-ATR problem from a holistic end-to-end perspective. A brief overview for the breadth of the SAR-ATR challenges is conducted. This is couched in terms of a single-channel SAR, and it is extendable to multi-channel SAR systems. Stages pertinent to the basic SAR-ATR system structure are defined, and the motivations of the requirements and constraints on the system constituents are addressed. For each stage in the SAR-ATR processing chain, a taxonomization methodology for surveying the numerous methods published in the open literature is proposed. Carefully selected works from the literature are presented under the taxa proposed. Novel comparisons, discussions, and comments are pinpointed throughout this paper. A two-fold benchmarking scheme for evaluating existing SAR-ATR systems and motivating new system designs is proposed. The scheme is applied to the works surveyed in this paper. Finally, a discussion is presented in which various interrelated issues, such as standard operating conditions, extended operating conditions, and target-model design, are addressed. This paper is a contribution toward fulfilling an objective of end-to-end SAR-ATR system design.