Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges

The deep learning, which is a dominating technique in artificial intelligence, has completely changed the image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications, and the future trends. Our review focuses on researches published from 2016 to the present, with a specific focus on deep learning-based approaches in the last five years. We divided all relegated algorithms into 3 categories, including classical image segmentation approach, machine learning-based approach and deep learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in 4 aspects including climate research, navigation, geographic information systems (GIS) production and others. It also provides insightful observations and inspiring future research directions.Comment: 24 pages, 6 figure

Classification of Compact Polarimetric Synthetic Aperture Radar Images

The RADARSAT Constellation Mission (RCM) was launched in June 2019. RCM, in addition to dual-polarization (DP) and fully quad-polarimetric (QP) imaging modes, provides compact polarimetric (CP) mode data. A CP synthetic aperture radar (SAR) is a coherent DP system in which a single circular polarization is transmitted followed by the reception in two orthogonal linear polarizations. A CP SAR fully characterizes the backscattered field using the Stokes parameters, or equivalently, the complex coherence matrix. This is the main advantage of a CP SAR over the traditional (non-coherent) DP SAR. Therefore, designing scene segmentation and classification methods using CP complex coherence matrix data is advocated in this thesis. Scene classification of remotely captured images is an important task in monitoring the Earth's surface. The high-resolution RCM CP SAR data can be used for land cover classification as well as sea-ice mapping. Mapping sea ice formed in ocean bodies is important for ship navigation and climate change modeling. The Canadian Ice Service (CIS) has expert ice analysts who manually generate sea-ice maps of Arctic areas on a daily basis. An automated sea-ice mapping process that can provide detailed yet reliable maps of ice types and water is desirable for CIS. In addition to linear DP SAR data in ScanSAR mode (500km), RCM wide-swath CP data (350km) can also be used in operational sea-ice mapping of the vast expanses in the Arctic areas. The smaller swath coverage of QP SAR data (50km) is the reason why the use of QP SAR data is limited for sea-ice mapping. This thesis involves the design and development of CP classification methods that consist of two steps: an unsupervised segmentation of CP data to identify homogeneous regions (superpixels) and a labeling step where a ground truth label is assigned to each super-pixel. An unsupervised segmentation algorithm is developed based on the existing Iterative Region Growing using Semantics (IRGS) for CP data and is called CP-IRGS. The constituents of feature model and spatial context model energy terms in CP-IRGS are developed based on the statistical properties of CP complex coherence matrix data. The superpixels generated by CP-IRGS are then used in a graph-based labeling method that incorporates the global spatial correlation among super-pixels in CP data. The classifications of sea-ice and land cover types using test scenes indicate that (a) CP scenes provide improved sea-ice classification than the linear DP scenes, (b) CP-IRGS performs more accurate segmentation than that using only CP channel intensity images, and (c) using global spatial information (provided by a graph-based labeling approach) provides an improvement in classification accuracy values over methods that do not exploit global spatial correlation

Arctic sea ice trafficability: new strategies for a changing icescape

Thesis (Ph.D.) University of Alaska Fairbanks, 2017Sea ice is an important part of the Arctic social-environmental system, in part because it provides a platform for human transportation and for marine flora and fauna that use the ice as a habitat. Sea ice loss projected for coming decades is expected to change ice conditions throughout the Arctic, but little is known about the nature and extent of anticipated changes and in particular potential implications for over-ice travel and ice use as a platform. This question has been addressed here through an extensive effort to link sea ice use and key geophysical properties of sea ice, drawing upon extensive field surveys around on-ice operations and local and Indigenous knowledge for the widely different ice uses and ice regimes of Utqiaġvik, Kotzebue, and Nome, Alaska. A set of nine parameters that constrain landfast sea ice use has been derived, including spatial extent, stability, and timing and persistence of landfast ice. This work lays the foundation for a framework to assess and monitor key ice-parameters relevant in the context of ice-use feasibility, safety, and efficiency, drawing on different remote-sensing techniques. The framework outlines the steps necessary to further evaluate relevant parameters in the context of user objectives and key stakeholder needs for a given ice regime and ice use scenario. I have utilized this framework in case studies for three different ice regimes, where I find uses to be constrained by ice thickness, roughness, and fracture potential and develop assessment strategies with accuracy at the relevant spatial scales. In response to the widely reported importance of high-confidence ice thickness measurements, I have developed a new strategy to estimate appropriate thickness compensation factors. Compensation factors have the potential to reduce risk of misrepresenting areas of thin ice when using point-based in-situ assessment methods along a particular route. This approach was tested on an ice road near Kotzebue, Alaska, where substantial thickness variability results in the need to raise thickness thresholds by 50%. If sea ice is thick enough for safe travel, then the efficiency of travel is relevant and is influenced by the roughness of the ice surface. Here, I develop a technique to derive trafficability measures from ice roughness using polarimetric and interferometric synthetic aperture radar (SAR). Validated using Structure-from-Motion analysis of imagery obtained from an unmanned aerial system near Utqiaġvik, Alaska, I demonstrate the ability of these SAR techniques to map both topography and roughness with potential to guide trail construction efforts towards more trafficable ice. Even when the ice is sufficiently thick to ensure safe travel, potential for fracturing can be a serious hazard through the ability of cracks to compromise load-bearing capacity. Therefore, I have created a state-of-the-art technique using interferometric SAR to assess ice stability with capability of assessing internal ice stress and potential for failure. In an analysis of ice deformation and potential hazards for the Northstar Island ice road near Prudhoe Bay on Alaska's North Slope I have identified a zone of high relative fracture intensity potential that conformed with road inspections and hazard assessments by the operator. Through this work I have investigated the intersection between ice use and geophysics, demonstrating that quantitative evaluation of a given region in the ice use assessment framework developed here can aid in tactical routing of ice trails and roads as well as help inform long-term strategic decision-making regarding the future of Arctic operations on or near sea ice

Scalable computing for earth observation - Application on Sea Ice analysis

In recent years, Deep learning (DL) networks have shown considerable improvements and have become a preferred methodology in many different applications. These networks have outperformed other classical techniques, particularly in large data settings. In earth observation from the satellite field, for example, DL algorithms have demonstrated the ability to learn complicated nonlinear relationships in input data accurately. Thus, it contributed to advancement in this field. However, the training process of these networks has heavy computational overheads. The reason is two-fold: The sizable complexity of these networks and the high number of training samples needed to learn all parameters comprising these architectures. Although the quantity of training data enhances the accuracy of the trained models in general, the computational cost may restrict the amount of analysis that can be done. This issue is particularly critical in satellite remote sensing, where a myriad of satellites generate an enormous amount of data daily, and acquiring in-situ ground truth for building a large training dataset is a fundamental prerequisite. This dissertation considers various aspects of deep learning based sea ice monitoring from SAR data. In this application, labeling data is very costly and time-consuming. Also, in some cases, it is not even achievable due to challenges in establishing the required domain knowledge, specifically when it comes to monitoring Arctic Sea ice with Synthetic Aperture Radar (SAR), which is the application domain of this thesis. Because the Arctic is remote, has long dark seasons, and has a very dynamic weather system, the collection of reliable in-situ data is very demanding. In addition to the challenges of interpreting SAR data of sea ice, this issue makes SAR-based sea ice analysis with DL networks a complicated process. We propose novel DL methods to cope with the problems of scarce training data and address the computational cost of the training process. We analyze DL network capabilities based on self-designed architectures and learn strategies, such as transfer learning for sea ice classification. We also address the scarcity of training data by proposing a novel deep semi-supervised learning method based on SAR data for incorporating unlabeled data information into the training process. Finally, a new distributed DL method that can be used in a semi-supervised manner is proposed to address the computational complexity of deep neural network training

Improving Flood Detection and Monitoring through Remote Sensing

As climate-change- and human-induced floods inflict increasing costs upon the planet, both in terms of lives and environmental damage, flood monitoring tools derived from remote sensing platforms have undergone improvements in their performance and capabilities in terms of spectral, spatial and temporal extents and resolutions. Such improvements raise new challenges connected to data analysis and interpretation, in terms of, e.g., effectively discerning the presence of floodwaters in different land-cover types and environmental conditions or refining the accuracy of detection algorithms. In this sense, high expectations are placed on new methods that integrate information obtained from multiple techniques, platforms, sensors, bands and acquisition times. Moreover, the assessment of such techniques strongly benefits from collaboration with hydrological and/or hydraulic modeling of the evolution of flood events. The aim of this Special Issue is to provide an overview of recent advancements in the state of the art of flood monitoring methods and techniques derived from remotely sensed data

Graph Force Learning

Features representation leverages the great power in network analysis tasks. However, most features are discrete which poses tremendous challenges to effective use. Recently, increasing attention has been paid on network feature learning, which could map discrete features to continued space. Unfortunately, current studies fail to fully preserve the structural information in the feature space due to random negative sampling strategy during training. To tackle this problem, we study the problem of feature learning and novelty propose a force-based graph learning model named GForce inspired by the spring-electrical model. GForce assumes that nodes are in attractive forces and repulsive forces, thus leading to the same representation with the original structural information in feature learning. Comprehensive experiments on benchmark datasets demonstrate the effectiveness of the proposed framework. Furthermore, GForce opens up opportunities to use physics models to model node interaction for graph learning