Oil Spill Candidate Detection Using a Conditional Random Field Model on Simulated Compact Polarimetric Imagery

This is an Accepted Manuscript of an article published by Taylor & Francis in Canadian Journal of Remote Sensing on 20 April 2022, available online: https://doi.org/10.1080/07038992.2022.2055534Although the compact polarimetric (CP) synthetic aperture radar (SAR) mode of the RADARSAT Constellation Mission (RCM) offers new opportunities for oil spill candidate detection, there has not been an efficient machine learning model explicitly designed to utilize this new CP SAR data for improved detection. This paper presents a conditional random field model based on the Wishart mixture model (CRF-WMM) to detect oil spill candidates in CP SAR imagery. First, a “Wishart mixture model” (WMM) is designed as the unary potential in the CRF-WMM to address the class-dependent information of oil spill candidates and oil-free water. Second, we introduce a new similarity measure based on CP statistics designed as a pairwise potential in the CRF-WMM model so that pixels with strong spatial connections have the same class label. Finally, we investigate three different optimization approaches to solve the resulting maximum a posterior (MAP) problem, namely iterated conditional modes (ICM), simulated annealing (SA), and graph cuts (GC). The results show that our proposed CRF-WMM model can delineate oil spill candidates better than the traditional CRF approaches and that the GC algorithm provides the best optimization.Natural Sciences and Engineering Research Council of Canada (NSERC),Grant RGPIN-2017-04869 || NSERC, Grant DGDND-2017-00078 || NSERC, Grant RGPAS2017-50794 || NSERC, Grant RGPIN-2019-06744

Spatial Modeling of Compact Polarimetric Synthetic Aperture Radar Imagery

The RADARSAT Constellation Mission (RCM) utilizes compact polarimetric (CP) mode to provide data with varying resolutions, supporting a wide range of applications including oil spill detection, sea ice mapping, and land cover analysis. However, the complexity and variability of CP data, influenced by factors such as weather conditions and satellite infrastructure, introduce signature ambiguity. This ambiguity poses challenges in accurate object classification, reducing discriminability and increasing uncertainty. To address these challenges, this thesis introduces tailored spatial models in CP SAR imagery through the utilization of machine learning techniques. Firstly, to enhance oil spill monitoring, a novel conditional random field (CRF) is introduced. The CRF model leverages the statistical properties of CP SAR data and exploits similarities in labels and features among neighboring pixels to effectively model spatial interactions. By mitigating the impact of speckle noise and accurately distinguishing oil spill candidates from oil-free water, the CRF model achieves successful results even in scenarios where the availability of labeled samples is limited. This highlights the capability of CRF in handling situations with a scarcity of training data. Secondly, to improve the accuracy of sea ice mapping, a region-based automated classification methodology is developed. This methodology incorporates learned features, spatial context, and statistical properties from various SAR modes, resulting in enhanced classification accuracy and improved algorithmic efficiency. Thirdly, the presence of a high degree of heterogeneity in target distribution presents an additional challenge in land cover mapping tasks, further compounded by signature ambiguity. To address this, a novel transformer model is proposed. The transformer model incorporates both fine- and coarse-grained spatial dependencies between pixels and leverages different levels of features to enhance the accuracy of land cover type detection. The proposed approaches have undergone extensive experimentation in various remote sensing tasks, validating their effectiveness. By introducing tailored spatial models and innovative algorithms, this thesis successfully addresses the inherent complexity and variability of CP data, thereby ensuring the accuracy and reliability of diverse applications in the field of remote sensing

Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements

This book is a reprint of the Special Issue entitled "Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements" that was published in Remote Sensing, MDPI. It provides insights into both core technical challenges and some selected critical applications of satellite remote sensing image analytics

Advanced Geoscience Remote Sensing

Nowadays, advanced remote sensing technology plays tremendous roles to build a quantitative and comprehensive understanding of how the Earth system operates. The advanced remote sensing technology is also used widely to monitor and survey the natural disasters and man-made pollution. Besides, telecommunication is considered as precise advanced remote sensing technology tool. Indeed precise usages of remote sensing and telecommunication without a comprehensive understanding of mathematics and physics. This book has three parts (i) microwave remote sensing applications, (ii) nuclear, geophysics and telecommunication; and (iii) environment remote sensing investigations

Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

Remote Sensing Applications in Coastal Environment

Coastal regions are susceptible to rapid changes, as they constitute the boundary between the land and the sea. The resilience of a particular segment of coast depends on many factors, including climate change, sea-level changes, natural and technological hazards, extraction of natural resources, population growth, and tourism. Recent research highlights the strong capabilities for remote sensing applications to monitor, inventory, and analyze the coastal environment. This book contains 12 high-quality and innovative scientific papers that explore, evaluate, and implement the use of remote sensing sensors within both natural and built coastal environments

A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Compensated Row-Column Ultrasound Imaging System

Ultrasound imaging is a valuable tool in many applications ranging from material science to medical imaging. While 2-D ultrasound imaging is more commonly used, 3-D ultrasound imaging offers unique opportunities that can only be found with the help of the extra dimension. Acquiring a 3-D ultrasound image can be done in two main ways: mechanically moving a transducer over a region of interest and using a fixed 2-D transducer. Mechanical motion introduces unwanted artifacts and increases image acquisition time, so a fixed 2-D is usually preferred. However, a fully addressed 2-D array will require a significant amount of connections and data to handle. This motivated the exploration of different simplification schemes to make 2-D arrays for 3-D ultrasound imaging feasible. A method that received a lot of attention for making real-time volumetric ultrasound imaging possible is the row-column method. The row-column method simplifies the fully addressed 2-D array by utilizing a set of 1-D arrays arranged in rows and another set in columns, one set will be responsible for transmit beamforming, while the other for receive beamforming. Using this setup, only $N+N$ connections are needed instead of $N\times N$. This simplification comes at the cost of image quality. Recent advances in row-column ultrasound imaging systems were largely focused on transducer design. However, these imaging systems face a few intrinsic challenges which cannot be addressed through transducer design alone: the issues of sparsity, speckle noise inherent to ultrasound, the spatially varying point spread function, and the ghosting artifacts inherent to the row-column method must all be taken into account. As such, strategies for tackling these intrinsic challenges in row-column imaging would be highly desired to improve imaging quality. In this thesis, we propose a novel compensated row-column ultrasound imaging system where the intrinsic characteristics of the transducer and other aspects of the physical row-column imaging apparatus are leveraged to computationally produce high quality ultrasound imagery. More specifically, the proposed system incorporates a novel conditional random field-driven computational image reconstruction component consisting of two phases: i) characterization and ii) compensation. In the characterization phase, a joint statistical image formation and noise model is introduced for characterizing the intrinsic properties of the physical row-column ultrasound imaging system. In the compensation phase, the developed joint image formation and noise model is incorporated alongside a conditional random field model within an energy minimization framework to reconstruct the compensated row-column ultrasound imagery. To explore the efficacy of the proposed concept, we introduced three different realizations of the proposed compensated row-column ultrasound imaging system. First, we introduce a compensated row-column imaging system based on a novel multilayered conditional random field driven framework to better account for local spatial relationships in the captured data. Second, we incorporated more global relationships by introducing a compensated row-column imaging system based around a novel edge-guided stochastically fully connected random field framework. Third, accounting for the case where the analytical image formation model may not optimally reflect the real-world physical system, we introduce a compensated row-column imaging system based around a data-driven spatially varying point-spread-function learning framework to better characterize the true physical image formation characteristics. While these different realizations of the compensated row-column system have their advantages and disadvantages, which will be discussed throughout this thesis, they all manage to boost the performance of the row-column method to comparable and often higher levels than the fully addressed 2-D array

Ocean Remote Sensing with Synthetic Aperture Radar

The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography