Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

Potential of nonlocally filtered pursuit monostatic TanDEM-X data for coastline detection

This article investigates the potential of nonlocally filtered pursuit monostatic TanDEM-X data for coastline detection in comparison to conventional TanDEM-X data, i.e. image pairs acquired in repeat-pass or bistatic mode. For this task, an unsupervised coastline detection procedure based on scale-space representations and K-medians clustering as well as morphological image post-processing is proposed. Since this procedure exploits a clear discriminability of "dark" and "bright" appearances of water and land surfaces, respectively, in both SAR amplitude and coherence imagery, TanDEM-X InSAR data acquired in pursuit monostatic mode is expected to provide a promising benefit. In addition, we investigate the benefit introduced by a utilization of a non-local InSAR filter for amplitude denoising and coherence estimation instead of a conventional box-car filter. Experiments carried out on real TanDEM-X pursuit monostatic data confirm our expectations and illustrate the advantage of the employed data configuration over conventional TanDEM-X products for automatic coastline detection

HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline

Deep learning-based coastline detection algorithms have begun to outshine traditional statistical methods in recent years. However, they are usually trained only as single-purpose models to either segment land and water or delineate the coastline. In contrast to this, a human annotator will usually keep a mental map of both segmentation and delineation when performing manual coastline detection. To take into account this task duality, we therefore devise a new model to unite these two approaches in a deep learning model. By taking inspiration from the main building blocks of a semantic segmentation framework (UNet) and an edge detection framework (HED), both tasks are combined in a natural way. Training is made efficient by employing deep supervision on side predictions at multiple resolutions. Finally, a hierarchical attention mechanism is introduced to adaptively merge these multiscale predictions into the final model output. The advantages of this approach over other traditional and deep learning-based methods for coastline detection are demonstrated on a dataset of Sentinel-1 imagery covering parts of the Antarctic coast, where coastline detection is notoriously difficult. An implementation of our method is available at \url{https://github.com/khdlr/HED-UNet}.Comment: This work has been accepted by IEEE TGRS for publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

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

Multidimensional and temporal SAR data representation and processing based on binary partition trees

This thesis deals with the processing of different types of multidimensional SAR data for distinct applications. Instead of handling the original pixels of the image, which correspond to very local information and are strongly contaminated by speckle noise, a region-based and multiscale data abstraction is defined, the Binary Partition Tree (BPT). In this representation, each region stands for an homogeneous area of the data, grouping pixels with similar properties and making easier its interpretation and processing. The work presented in this thesis concerns the definition of the BPT structures for Polarimetric SAR (PolSAR) images and also for temporal series of SAR acquisitions. It covers the description of the corresponding data models and the algorithms for BPT construction and its exploitation. Particular attention has been paid to the speckle filtering application. The proposed technique has proven to achieve arbitrarily large regions over homogeneous areas while also preserving the spatial resolution and the small details of the original data. As a consequence, this approach has demonstrated an improvement in the performance of the target response estimation with respect to other speckle filtering techniques. Moreover, due to the flexibility and convenience of this representation, it has been employed for other applications as scene segmentation and classification. The processing of SAR time series has also been addressed, proposing different approaches for dealing with the temporal information of the data, resulting into distinct BPT abstractions. These representations have allowed the development of speckle filtering techniques in the spatial and temporal domains and also the improvement and the definition of additional methods for classification and temporal change detection and characterization

한반도 주변해 연안 해양현상에 대한 합성개구레이더 활용

학위논문 (박사)-- 서울대학교 대학원 : 과학교육과 (지구과학전공), 2016. 8. 박경애.In this thesis, the applicability of synthetic aperture radar (SAR) to interpretation of oceanic phenomena at the coastal regions around Korea peninsula is presented. For that, the spatial and temporal variations of SAR-derived coastal wind fields and evolution of disastrous oil spills on SAR images were analyzed in relation to atmospheric and oceanic environmental factors using in-situ measurement and satellite observations. The SAR wind fields retrieved from the east coast of Korea in August 2007 during the upwelling period revealed a spatial distinction between near and offshore regions. Low wind speeds were associated with cold water regions with dominant coastal upwelling. Time series of in-situ measurements of both wind speed and water temperature indicated that the upwelling was induced by the wind field. SAR data at the present upwelling region showed a relatively large backscattering attenuation to SST ratio of 1.2 dB ºC−1 compared the known dependence of the water viscosity on the radar backscattering. In addition, wind speed magnitude showed a positive correlation with the difference between SST and air temperature. It implies that the low wind field from SAR was mainly induced by changes in atmospheric stability due to air-sea temperature differences. Oil spills at the Hebei Spirit accident off the coast of Korea in the Yellow Sea were identified using SAR data and their evolution was investigated. To quantitatively analyze the spatial and temporal variations of oil spills, objective detection methods based on adaptive thresholding and a neural network were applied. Prior to applying, the results from two methods were compared for verification. It showed good agreement enough for the estimation of the extent of oil patches and their trajectories, with the exception of negligible errors at the boundaries. Quantitative analyses presented that the detected oil slicks moved southeastward, corresponding to the prevailing wind and tidal currents, and gradually dissipated during the spill, except for an extraordinary rapid decrease in onshore regions at the initial stage. It was identified that the initial dissipation of the spilt oil was induced by strong tidal mixing in the tidal front zone from comparison with the tidal mixing index. The spatial and temporal variations of the oil slicks confirmed the influence of atmospheric and oceanic environmental factors. The overall horizontal migration of the oil spills detected from consecutive SAR images was mainly driven by Ekman drift during the winter monsoon rather than the tidal residual current.Chapter 1. Introduction 1 1.1. Study Background 1 1.2. Objectives of the Thesis 14 Chapter 2. Data Description 15 2.1. SAR Data 15 2.2. Other Satellite Data 21 2.2.1. Wind Data 21 2.2.2. Sea Surface Temperature Data 21 2.2.3. Ocean Color Data 22 2.3. Reanalysis Data 23 2.4. In-situ Measurements 23 2.5. Land Masking Data 26 2.6. Tidal Current Data 28 Chapter 3. Methods 29 3.1. SAR Wind Retrieval 29 3.2. Noise Reduction of ScanSAR Images 37 3.3. Conversion of Wind Speed to Neutral Wind 41 3.4. Estimation of Index of the Tidal Front 43 3.5. Estimation of Ekman Drift and Tidal Residual Current 45 3.6. Feature Detection Methods 46 3.6.1. Adaptive Threshold Method 47 3.6.2. Bimodal Histogram Method 50 3.6.3. Neural Network Method 54 Chapter 4. Coastal Wind Fields and Upwelling Response 58 4.1. Variations of Wind Fields during Coastal Upwelling 58 4.2. Stability Effect on Wind Speed 65 4.3. Biological Impact of Upwelling 70 Chapter 5. Characteristics of Objective Feature Detection 74 5.1. Comparison of Thresholding Methods 74 5.2. Oil Spill of the Hebei Spirit by Thresholding Method 81 5.3. Oil Spill by the Hebei Spirit by Neural Network Method 85 5.4. Differences by Detection Methods 88 Chapter 6. Evolution of Oil Spill at the Coastal Region 90 6.1. Temporal Evolution of the Hebei Spirit Oil Spill 90 6.2. Effect of Artificial Factor on the Evolution 96 Chapter 7. Effect of Environmental Factors on the Oil Spill 98 7.1. Effect of Tidal Mixing 98 7.2. Effect of Wind and Tidal Current 103 Chapter 8. Summary and Conclusion 110 Reference 114 Abstract in Korean 142Docto

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

About Edge Detection in Digital Images

Edge detection is one of the most commonly used procedures in digital image processing. In the last 30-40 years, many methods and algorithms for edge detection have been proposed. This article presents an overview of edge detection methods, the methods are divided according to the applied basic principles. Next, the measures and image database used for edge detectors performance quantification are described. Ordinary users as well as authors proposing new edge detectors often use Matlab function without understanding it in details. Therefore, one chapter is devoted to some of Matlab function parameters that affect the final result. Finally, the latest trends in edge detection are listed. Picture Lena and two images from Berkeley segmentation data set (BSDS500) are used for edge detection methods comparison

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

Dark Spot Detection from SAR Intensity Imagery with Spatial Density Thresholding for Oil Spill Monitoring

Since the 1980s, satellite-borne synthetic aperture radar (SAR) has been investigated for early warning and monitoring of marine oil spills to permit effective satellite surveillance in the marine environment. Automated detection of oil spills from satellite SAR intensity imagery consists of three steps: 1) Detection of dark spots; 2) Extraction of features from the detected dark spots; and 3) Classification of the dark spots into oil spills and look-alikes. However, marine oil spill detection is a very difficult and challenging task. Open questions exist in each of the three stages. In this thesis, the focus is on the first stage—dark spot detection. An efficient and effective dark spot detection method is critical and fundamental for developing an automated oil spill detection system. A novel method for this task is presented. The key to the method is utilizing the spatial density feature to enhance the separability of dark spots and the background. After an adaptive intensity thresholding, a spatial density thresholding is further used to differentiate dark spots from the background. The proposed method was applied to a evaluation dataset with 60 RADARSAT-1 ScanSAR Narrow Beam intensity images containing oil spill anomalies. The experimental results obtained from the test dataset demonstrate that the proposed method for dark spot detection is fast, robust and effective. Recommendations are given for future research to be conducted to ensure that this procedure goes beyond the prototype stage and becomes a practical application