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Deep learning (DL), mainly composed of deep and complex neural networks such as recurrent network and convolutional network, is an emerging research branch in the field of artificial intelligence and machine learning. DL revolution has a far-reaching impact on all scientific disciplines and every corner of our lives. With continuing technological advances, marine science is entering into the big data era with the exponential growth of information. DL is an effective means of harnessing the power of big data. Combined with unprecedented data from cameras, acoustic recorders, satellite remote sensing, and large model outputs, DL enables scientists to solve complex problems in biology, ecosystems, climate, energy, as well as physical and chemical interactions. Although DL has made great strides, it is still only beginning to emerge in many fields of marine science, especially towards representative applications and best practices for the automatic analysis of marine organisms and marine environments. DL in nowadays' marine science mainly leverages cutting-edge techniques of deep neural networks and massive data which collected by in-situ optical or acoustic imaging sensors for underwater applications, such as plankton classification and coral reef detection. This research topic aims to expand the applications of marine science to cover all aspects of detection, classification, segmentation, localization, and density estimation of marine objects, organisms, and phenomena.
This book gives a start-to-finish overview of the whole Fish4Knowledge project, in 18 short chapters, each describing one aspect of the project. The Fish4Knowledge project explored the possibilities of big video data, in this case from undersea video. Recording and analyzing 90 thousand hours of video from ten camera locations, the project gives a 3 year view of fish abundance in several tropical coral reefs off the coast of Taiwan. The research system built a remote recording network, over 100 Tb of storage, supercomputer processing, video target detection and tracking, fish species recognition and analysis, a large SQL database to record the results and an efficient retrieval mechanism. Novel user interface mechanisms were developed to provide easy access for marine ecologists, who wanted to explore the dataset. The book is a useful resource for system builders, as it gives an overview of the many new methods that were created to build the Fish4Knowledge system in a manner that also allows readers to see how all the components fit together.
A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.
This book constitutes the refereed proceedings of the 18th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2017, held in Antwerp, Belgium, in September 2017. The 63 full papers presented in this volume were carefully selected from 134 submissions. They deal with human-computer interaction; classification and recognition; navigation, mapping, robotics, and transports; video processing and retrieval; security, forensics, surveillance; and image processing.
This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm’s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.
Computer vision has been successful in several important applications recently. Vision techniques can now be used to build very good models of buildings from pictures quickly and easily, to overlay operation planning data on a neuros- geon’s view of a patient, and to recognise some of the gestures a user makes to a computer. Object recognition remains a very di cult problem, however. The key questions to understand in recognition seem to be: (1) how objects should be represented and (2) how to manage the line of reasoning that stretches from image data to object identity. An important part of the process of recognition { perhaps, almost all of it { involves assembling bits of image information into helpful groups. There is a wide variety of possible criteria by which these groups could be established { a set of edge points that has a symmetry could be one useful group; others might be a collection of pixels shaded in a particular way, or a set of pixels with coherent colour or texture. Discussing this process of grouping requires a detailed understanding of the relationship between what is seen in the image and what is actually out there in the world.
Our species is more profoundly connected to the sea than we ever realized, as an intrepid cadre of scientists, athletes, and explorers is now discovering. Deep follows these adventurers into the ocean to report on the latest findings about its wondrous biology -- and unimagined human abilities.
This edited volume is the premier book dedicated exclusively to marine science education and improving ocean literacy, aiming to showcase exemplary practices in marine science education and educational research in this field on a global scale. It informs, inspires, and provides an intellectual forum for practitioners and researchers in this particular context. Subject areas include sections on marine science education in formal, informal and community settings. This book will be useful to marine science education practitioners (e.g. formal and informal educators) and researchers (both education and science).
Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often “messy” and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems. Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.
The recent launches of three fully polarimetric synthetic aperture radar (PolSAR) satellites have shown that polarimetric radar imaging can provide abundant data on the Earth’s environment, such as biomass and forest height estimation, snow cover mapping, glacier monitoring, and damage assessment. Written by two of the most recognized leaders in this field, Polarimetric Radar Imaging: From Basics to Applications presents polarimetric radar imaging and processing techniques and shows how to develop remote sensing applications using PolSAR imaging radar. The book provides a substantial and balanced introduction to the basic theory and advanced concepts of polarimetric scattering mechanisms, speckle statistics and speckle filtering, polarimetric information analysis and extraction techniques, and applications typical to radar polarimetric remote sensing. It explains the importance of wave polarization theory and the speckle phenomenon in the information retrieval problem of microwave imaging and inverse scattering. The authors demonstrate how to devise intelligent information extraction algorithms for remote sensing applications. They also describe more advanced polarimetric analysis techniques for polarimetric target decompositions, polarization orientation effects, polarimetric scattering modeling, speckle filtering, terrain and forest classification, manmade target analysis, and PolSAR interferometry. With sample PolSAR data sets and software available for download, this self-contained, hands-on book encourages you to analyze space-borne and airborne PolSAR and polarimetric interferometric SAR (Pol-InSAR) data and then develop applications using this data.