Sea Ice Image Processing with Matlab(r)

ISBN-13: 9781138032668 / Angielski / Twarda / 2018 / 242 str.

Qin Zhang; Roger Skjetne

Opis

The ice concentration data on a global scale are available on a daily basis due to microwave satellite sensors. Image processing for sea ice is vital to estimate the sea ice properties and understand the behavior of sea ice especially on a relatively small scale. Currently, sea ice concentration is estimated from lower-resolution passive microwave sensors. This book aims to present the most recent image processing techniques including the so-called gradient vector flow snake algorithm to identify individual ice floes from the ice images. It makes an extensive use of this powerful technique to extract or estimate the sea-ice parameters and to develop tools useful to scientists and engineers.

Recenzje

"Sea Ice Image Processing with MATLAB® is a large and important step forward in the creation of the necessary tools for the planning and execution of Arctic maritime operations. The ability to predict what kind of ice that one will encounter is of critical importance for safety, as you need to know what you are going to run into. This book provides an important insight to the cutting edge of technology being developed for sustainable operations in the polar regions."
—Åke Rohlén, Arctic Marine Solutions AB, Sweden

"This is a really elaborate book for the image processing to extract ice information. To my knowledge, this is the first work that describes the methods to obtain ice floe size distribution in a systematic and sophisticated way. It is still a big issue to predict the sea ice behavior in the numerical sea ice model due to the lack of our knowledge about the sub-grid scale information of sea ice, especially floe size distribution. This book aims at solving this problem and has achieved it to some extent. This work certainly contributes to this issue and sheds light on collecting observational data on an operational basis. Therefore, I sincerely hope the algorithm developed by the authors will be used by many people to improve our understanding of sea ice properties."
— Takenobu Toyota, Hokkaido University, Sapporo, Japan

Spis treści

Introduction; Digital Image Processing Preliminaries; Ice Pixel Detection; Ice Edge Detection; Watershed-Based Ice Floe Segmentation; GVF Snake-Based Ice Floe Boundary Identification and Ice Image Segmentation; Sea Ice Type Identification; Sea Ice Image Processing Applications; Model Sea Ice Image Processing Applications; Appendix  A Geometric Calibration; Appendix B  Ice Image Data Structure.

O autorze

Qin Zhang received her Ph.D. degree in 2015 at the Norwegian University of Science and Technology (NTNU), Trondheim, Norway. She became a researcher in 2015 at the Center for Research-based Innovation (CRI) Sustainable Arctic Marine and Coastal Technology (SAMCoT), NTNU. Her research interests include remote sensing, image and sensory data processing.

Prof. Roger Skjetne received his MSc degree in 2000 in control engineering at the University of California at Santa Barbara, and his PhD degree in 2005 at the Norwegian University of Science and Technology (NTNU), for which his thesis was awarded the Exxon Mobil prize for best PhD thesis in applied research. Prior to his studies, he worked as an electrician for Aker Elektro AS on numerous oil installations for the North Sea. In 2004-2009 he was employed in Marine Cybernetics AS, working on Hardware-In-the-Loop (HIL) simulation for testing safety-critical marine control systems. From August 2009 he has held the position of Professor in Marine Control Engineering at the Department of Marine Technology at NTNU, where he presently is the leader of the research group on Marine Structures. His research interests are within Arctic stationkeeping operations and Ice Management systems for ships and rigs, environmentally robust control of shipboard electric power systems, and nonlinear control theory for motion control of single and groups of marine vessels. Roger Skjetne is leader of the ice management work package in the CRI Sustainable Arctic Marine and Coastal Technology (SAMCoT), associated researcher in the CoE Centre for Ships and Ocean Structures (CeSOS) and CoE Autonomous Marine Operations and Systems (AMOS), principal researcher in the CRI on Marine Operations (MOVE), and he was project manager for the KMB Arctic DP research project. He is also co-founder of the two companies BluEye Robotics and ArcISo.