Synthetic Aperture Radar Imaging Mechanism for Oil Spills delivers the critical tool needed to understand the latest technology in radar imaging of oil spills, particularly microwave radar as a main source to understand analysis and applications in the field of marine pollution. Filling the gap between modern physics quantum theory and applications of radar imaging of oil spills, this reference is packed with technical details associated with the potentiality of synthetic aperture radar (SAR) and the key methods used to extract the value-added information necessary, such as location, size, perimeter and chemical details of the oil slick from SAR measurements. Rounding out with practical simulation trajectory movements of oil spills using radar images, this book brings an effective new source of technology and applications for today's oil and marine pollution engineers. - Bridges the gap between theory and application of the techniques involving oil spill monitoring - Helps readers understand a new approach to four-dimensional automatic detection - Provides advanced knowledge on image processing based on intelligent learning machine algorithms and new techniques for detection, such as quantum and multi-objective algorithms

Synthetic Aperture Radar Automatic Detection Algorithms (SARADA) for Oil Spills conveys the pivotal tool required to fully comprehend the advanced algorithms in radar monitoring and detection of oil spills, particularly quantum computing and algorithms as a keystone to comprehending theories and algorithms behind radar imaging and detection of marine pollution. Bridging the gap between modern quantum mechanics and computing detection algorithms of oil spills, this book contains precise theories and techniques for automatic identification of oil spills from SAR measurements. Based on modern quantum physics, the book also includes the novel theory on radar imaging mechanism of oil spills. With the use of precise quantum simulation of trajectory movements of oil spills using a sequence of radar images, this book demonstrates the use of SARADA for contamination by oil spills as a promising novel technique. Key Features: Introduces basic concepts of a radar remote sensing. Fills a gap in the knowledge base of quantum theory and microwave remote sensing. Discusses the important aspects of oil spill imaging in radar data in relation to the quantum theory. Provides recent developments and progresses of automatic detection algorithms of oil spill from radar data. Presents 2-D oil spill radar data in 4-D images.

This report provides a detailed evaluation of synthetic aperture radar (SAR) as a potential technology improvement over the Coast Guard's existing side-looking airborne radar (SLAR) for oil-spill surveillance applications. The U.S. Coast Guard Research and Development Center (R & D Center), Environmental Safety Branch, sponsored a joint experiment including the U.S. Coast Guard, Sandia National Laboratories, and the National Oceanographic and Atmospheric Administration (NOAA), Hazardous Materials Division. Radar imaging missions were flown on six days over the coastal waters off Santa Barbara, CA, where there are constant natural seeps of oil. Both the Coast Guard SLAR and the Sandia National Laboratories SAR were employed to acquire simultaneous images of oil slicks and other natural sea surface features that impact oil-spill interpretation. Surface truth and other environmental data were also recorded during the experiment. The experiment data were processed at Sandia National Laboratories and delivered to the R & D Center on a PC-based computer workstation for analysis by experiment participants.

Operational discharges of oil from vessels, whether accidental or deliberate, are a growing concern as the levels of maritime traffic increase. Oil tankers and other kinds of ships are among the suspected offenders of illegal discharges. The international legislation contains minor and well-defined exceptions related to ocean areas (internal waters, marine protected areas, MARPOL aÌ22́Ơ¿3specialaÌ22́Ơ℗+ areas, territorial seas or exclusive economic zones). These areas often determine whether an action is considered legal or not and define the rights and obligations, including law enforcement obligations. Synthetic aperture radar (SAR) is the most used remote sensing tool for monitoring oil pollution over vast ocean areas. SAR is an active microwave RS sensor capable of taking measurements day or night and almost independently from atmospheric conditions. Manual oil spill detection in a SAR image is ordinarily done by a trained human interpreter who visually inspects SAR images for any possible spills. However, manual inspection can be time-consuming, biased, inconsistent and subjective. A faster and more robust alternative is to use automated image processing and machine learning methods. The current automated oil detection methods, however, are still not ideal and there is still a need for improvement. Also, data costs have resulted in limited studies on oil spill detection in African oceans. The launch of several Sentinel missions with SAR sensors has considerably improved coverage and accessibility of data over African oceans. The goal of the study is to develop an automated detection of oil spill discharges from vessels in African seas using the freely available Sentinel SAR data. A novel oil spill detection framework that can detect possible oil spill candidates and remove unwanted detections (i.e., false positives) was proposed. The framework used a novel linear dark spot detection algorithm and an improved oil spill discrimination process. The linear detection process used a segmentation-based algorithm to isolate linear dark spots (potential oil spills) from other features in the image. The process involved a more efficient feature selection and classification process. The proposed linear detection algorithm was evaluated for detection accuracy and compared to other segmentation-based oil spill detection algorithms, including state-of-the-art oil spill detection methods. The results demonstrated the proposed approach to be a more efficient and robust linear dark spot detection method. An improved discrimination process was presented to reduce false detections from a segmentation-based algorithm. The selection of relevant oil spill features depends on many factors which could influence the accuracy of the classification task. Automated features selection methods were thus considered to improve the discrimination process. Using feature selection, the most significant oil spill features with minimum variations were determined. The significant features were used as input vectors to classify oil spill events from moving vessels. An optimised Gradient Boosting Tree Classifier (GBT) was used for the classification task. The proposed novel framework showed promising results for monitoring oil spill from moving vessels using SAR in African oceans on a regular basis. Future work includes adding a confidence measure and alert level estimation. The system will incorporate ancillary information such as the oil spill source and the sensitivity of the polluted area to measure environmental impact.

Advanced Algorithms for Mineral and Hydrocarbon Exploration Using Synthetic Aperture Radar is a research- and practically-based reference that bridges the gap between the remote sensing industry and the mineral and hydrocarbon exploration industry. In this context, the book explains how to commercialize the applications of synthetic aperture radar and quantum interferometry synthetic aperture radar (QInSAR) for mineral and hydrocarbon exploration. This multidisciplinary reference is useful for oil and gas companies, the mining industry, geoscientists, and coastal and petroleum engineers. Presents both theoretical and practical applications of various types of remote sensing for hydrocarbon and mineral exploration Covers specific problems for exploration professionals and provides applications for solving each problem Includes more than 100 images and figures to help explain the concepts and applications described in the book

Advanced Algorithms for Mineral and Hydrocarbon Exploration Using Synthetic Aperture Radar is a research- and practically-based reference that bridges the gap between the remote sensing industry and the mineral and hydrocarbon exploration industry. In this context, the book explains how to commercialize the applications of synthetic aperture radar and quantum interferometry synthetic aperture radar (QInSAR) for mineral and hydrocarbon exploration. This multidisciplinary reference is useful for oil and gas companies, the mining industry, geoscientists, and coastal and petroleum engineers. - Presents both theoretical and practical applications of various types of remote sensing for hydrocarbon and mineral exploration - Covers specific problems for exploration professionals and provides applications for solving each problem - Includes more than 100 images and figures to help explain the concepts and applications described in the book