Detection of Ship Using Image Processing and Neural Network

Indonesia is one of the countries in this world that has the most outstanding fishery potential. There are more than 3000 fish species under Indonesia's sea, yet the people are still not able to relish them completely. Illegal fishing by foreign ships in Indonesia's territorial sea is one of the reasons why this happens. In order to minimize this kind of loss, those ships should be detected automatically by implementing image processing and artificial intelligence techniques. The study proposed techniques for automatic detection of ships at sea on digital images. These techniques are global image thresholding and artificial neural network backpropagation. The result of this research is proposed of technique able to detect ship with 85% accuracy level. This method may be improved by adding more training data varieties

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

Radar Technology

In this book “Radar Technology”, the chapters are divided into four main topic areas: Topic area 1: “Radar Systems” consists of chapters which treat whole radar systems, environment and target functional chain. Topic area 2: “Radar Applications” shows various applications of radar systems, including meteorological radars, ground penetrating radars and glaciology. Topic area 3: “Radar Functional Chain and Signal Processing” describes several aspects of the radar signal processing. From parameter extraction, target detection over tracking and classification technologies. Topic area 4: “Radar Subsystems and Components” consists of design technology of radar subsystem components like antenna design or waveform design

Détection de bateaux dans les images de radar à ouverture synthétique

Le but principal de cette thèse est de développer des algorithmes efficaces et de concevoir un système pour la détection de bateaux dans les images Radar à Ouverture Synthetique (ROS.) Dans notre cas, la détection de bateaux implique en premier lieu la détection de cibles de points dans les images ROS. Ensuite, la détection d'un bateau proprement dit dépend des propriétés physiques du bateau lui-même, tel que sa taille, sa forme, sa structure, son orientation relative a la direction de regard du radar et les conditions générales de l'état de la mer. Notre stratégie est de détecter toutes les cibles de bateaux possibles dans les images de ROS, et ensuite de chercher autour de chaque candidat des évidences telle que les sillons. Les objectifs de notre recherche sont (1) d'améliorer 1'estimation des paramètres dans Ie modèle de distribution-K et de déterminer les conditions dans lesquelles un modèle alternatif (Ie Gamma, par exemple) devrait être utilise plutôt; (2) d'explorer Ie modèle PNN (Probabilistic Neural Network) comme une alternative aux modèles paramétriques actuellement utilises; (3) de concevoir un modèle de regroupement flou (FC : Fuzzy Clustering) capable de détecter les petites et grandes cibles de bateaux dans les images a un seul canal ou les images a multi-canaux; (4) de combiner la détection de sillons avec la détection de cibles de bateaux; (5) de concevoir un modèle de détection qui peut être utilisé aussi pour la détection des cibles de bateaux en zones costières.Abstract: The main purpose of this thesis is to develop efficient algorithms and design a system for ship detection from Synthetic Aperture Radar (SAR) imagery. Ship detection usually involves through detection of point targets on a radar clutter background.The detection of a ship depends on the physical properties of the ship itself, such as size, shape, and structure; its orientation relative to the radar look-direction; and the general condition of the sea state. Our strategy is to detect all possible ship targets in SAR images, and then search around each candidate for the wake as further evidence.The objectives of our research are (1) to improve estimation of the parameters in the K-distribution model and to determine the conditions in which an alternative model (Gamma, for example) should be used instead; (2) to explore a PNN (Probabilistic Neural Networks) model as an alternative to the commonly used parameteric models; (3) to design a FC (Fuzzy Clustering) model capable of detecting both small and large ship targets from single-channel images or multi-channel images; (4) to combine wake detection with ship target detection; (5) to design a detection model that can also be used to detect ship targets in coastal areas. We have developed algorithms for each of these objectives and integrated them into a system comprising six models.The system has been tested on a number of SAR images (SEASAT, ERS and RADARSAT-1, for example) and its performance has been assessed

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

A Study on the Recognition of Seabed Environments Employing Sonar Images

The ocean accounts for approximately 70% of the area on the earth, and the water as well as coastal areas sustain many species including humans. Ocean resources are used for fish farming, land reclamation, and a variety of other purposes. Seabed resources such as oil, natural gas methane hydrates, and manganese nodules are still largely unexploited on the bottom of the sea. Maps are critical to development activities such as construction, mining, offshore drilling, marine traffic control, security, environmental protection, and tourism. Accordingly, more topographic and others types of mapping information are needed for marine and submarine investigations. Both waterborne and airborne survey techniques show promise for collecting data on marine and submarine environments, and these techniques can be classified into four main categories. First, remote sensing by satellites or aircraft is a widely used technique that can yield important data such as information on sea levels and coastal sediment transport. Second, investigations may collect direct information by remotely operated vehicles (ROVs), autonomous underwater vehicles (AUVs), and divers. While the quality of data obtained from these techniques is high, the data obtained are often limited to relatively shallow and small geographic areas. Third, sediment profile imagery can be used to collect photographs that contain detailed information about the seabed. Lastly, acoustic investigations that use sonar are popular in marine mapping studies, especially in coastal areas. In particular, acoustic investigations that employ ultrasound technology can yield rich information about variations in bathymetry. Unlike air, water has physical properties that make it difficult for light or electromagnetic waves to pass through. However, sound waves propagate readily in water. Therefore, sound waves are used in a wide range of technical applications to detect underwater structures that are difficult to observe with light-based techniques. In the dark depths of the ocean, the use of acoustic technology is essential. The development of marine acoustic technology is expanding in modern times. In addition to the basic physics related to acoustic waves, much research has been dedicated to other basic and applied fields such as electronics, physical oceanography, signal processing, and biology. The realization of new sonar systems that utilize advanced detection algorithms can be expected to contribute to major breakthroughs in oceanographic research that require deployment to novel marine environments and other areas of natural resource interest. In this study, the author focuses on side-scan sonar, which is one of the imaging technologies that employs sound to determine the seabed state, to conduct research on imaging algorithms for discrimination. The proposed method for discrimination was coupled to a high-speed detection method for installed reefs on the seabed. This method is also capable of detecting unknown objects with Haar-like features during object recognition of rectangular regions of a certain size via machine learning by AdaBoost and fast elimination of non-object regions on the cascade structure. Side-scan and forward looking sonars are some of the most widely used imaging systems for obtaining large-scale images of the seafloor, and their application continues to expand rapidly with their increasing deployment on AUVs. However, it can be difficult to extract quantitative information from the images generated from these processes, in particular, for the detection and extraction of information on the objects within these images. Hence, this study analyzes features that are common to most undersea objects projected in side-scan sonar images to improve information processing. By using a technique based on the k-means method to determine the Haar-like features, the number of patterns of Haar-like features was minimized and the proposed method was capable of detecting undersea objects faster than current methodology. This study demonstrates the effectiveness of this method by applying it to the detection of real objects imaged on the seabed (i.e., sandy ground and muddy ground). Attempts are made as well to automate the proposed method for discriminating objects lying on the seafloor from surficial sediments. During undersea exploration, a thorough understanding of the state of the seafloor surrounding objects of interest is important. Therefore, a method is proposed in this study to automatically determine seabed sediment characteristics. In traditional methods, a variety of techniques have been used to collect information about seabed sediments including depth measurements, bathymetry evaluations, and seabed image analyses using the co-occurrence direction of the gray values of the image. Unfortunately, such data cannot be estimated from the object image itself and it can take a long time to obtain the required information. Therefore, these techniques are not currently suitable for real-time identification of objects on the seafloor. For practical purposes, automatic techniques that are developed should follow a simple procedure that results in highly precise and accurate classifications. The technique proposed here uses the subspace method, which is a method that has been used for supervised pattern recognition and analyses of higher-order local autocorrelation features. The most important feature of this method is that it uses only acoustic images obtained from the side-scan sonar. This feature opens up the possibility of installing this technology in unmanned small digital devices. In this study, the classification accuracy of the proposed automation method is compared to the accuracy of traditional methods in order to show the usefulness of the technology. In addition, the proposed method is applied to real-world images of the seabed to evaluate its effectiveness in marine surveys. The thesis is organized as follows. In Chapter 1, the purpose of this study is presented and previous studies relevant to this research are reviewed. In Chapter 2, an overview of underwater sound is given and key principles of sound wave technology are explained. In Chapter 3, a new method for detecting and discriminating objects on the seafloor is proposed. In Chapter 4, the possibility of automating the discrimination method is explored. Finally, Chapter 5 summarizes the findings of this study and proposes new avenues for future research.九州工業大学博士学位論文 学位記番号:工博甲第364号　学位授与年月日:平成26年3月25日Chapter 1 Introduction|Chapter 2 Underwater acoustics|Chapter 3 Detection of underwater objects based on machine learning|Chapter 4 Automatic classification of seabed sediments using HLAC|Chapter 5 Conclusion九州工業大学平成25年

A Study on the Recognition of Seabed Environments Employing Sonar Images

The ocean accounts for approximately 70% of the area on the earth, and the water as well as coastal areas sustain many species including humans. Ocean resources are used for fish farming, land reclamation, and a variety of other purposes. Seabed resources such as oil, natural gas methane hydrates, and manganese nodules are still largely unexploited on the bottom of the sea. Maps are critical to development activities such as construction, mining, offshore drilling, marine traffic control, security, environmental protection, and tourism. Accordingly, more topographic and others types of mapping information are needed for marine and submarine investigations. Both waterborne and airborne survey techniques show promise for collecting data on marine and submarine environments, and these techniques can be classified into four main categories. First, remote sensing by satellites or aircraft is a widely used technique that can yield important data such as information on sea levels and coastal sediment transport. Second, investigations may collect direct information by remotely operated vehicles (ROVs), autonomous underwater vehicles (AUVs), and divers. While the quality of data obtained from these techniques is high, the data obtained are often limited to relatively shallow and small geographic areas. Third, sediment profile imagery can be used to collect photographs that contain detailed information about the seabed. Lastly, acoustic investigations that use sonar are popular in marine mapping studies, especially in coastal areas. In particular, acoustic investigations that employ ultrasound technology can yield rich information about variations in bathymetry. Unlike air, water has physical properties that make it difficult for light or electromagnetic waves to pass through. However, sound waves propagate readily in water. Therefore, sound waves are used in a wide range of technical applications to detect underwater structures that are difficult to observe with light-based techniques. In the dark depths of the ocean, the use of acoustic technology is essential. The development of marine acoustic technology is expanding in modern times. In addition to the basic physics related to acoustic waves, much research has been dedicated to other basic and applied fields such as electronics, physical oceanography, signal processing, and biology. The realization of new sonar systems that utilize advanced detection algorithms can be expected to contribute to major breakthroughs in oceanographic research that require deployment to novel marine environments and other areas of natural resource interest. In this study, the author focuses on side-scan sonar, which is one of the imaging technologies that employs sound to determine the seabed state, to conduct research on imaging algorithms for discrimination. The proposed method for discrimination was coupled to a high-speed detection method for installed reefs on the seabed. This method is also capable of detecting unknown objects with Haar-like features during object recognition of rectangular regions of a certain size via machine learning by AdaBoost and fast elimination of non-object regions on the cascade structure. Side-scan and forward looking sonars are some of the most widely used imaging systems for obtaining large-scale images of the seafloor, and their application continues to expand rapidly with their increasing deployment on AUVs. However, it can be difficult to extract quantitative information from the images generated from these processes, in particular, for the detection and extraction of information on the objects within these images. Hence, this study analyzes features that are common to most undersea objects projected in side-scan sonar images to improve information processing. By using a technique based on the k-means method to determine the Haar-like features, the number of patterns of Haar-like features was minimized and the proposed method was capable of detecting undersea objects faster than current methodology. This study demonstrates the effectiveness of this method by applying it to the detection of real objects imaged on the seabed (i.e., sandy ground and muddy ground). Attempts are made as well to automate the proposed method for discriminating objects lying on the seafloor from surficial sediments. During undersea exploration, a thorough understanding of the state of the seafloor surrounding objects of interest is important. Therefore, a method is proposed in this study to automatically determine seabed sediment characteristics. In traditional methods, a variety of techniques have been used to collect information about seabed sediments including depth measurements, bathymetry evaluations, and seabed image analyses using the co-occurrence direction of the gray values of the image. Unfortunately, such data cannot be estimated from the object image itself and it can take a long time to obtain the required information. Therefore, these techniques are not currently suitable for real-time identification of objects on the seafloor. For practical purposes, automatic techniques that are developed should follow a simple procedure that results in highly precise and accurate classifications. The technique proposed here uses the subspace method, which is a method that has been used for supervised pattern recognition and analyses of higher-order local autocorrelation features. The most important feature of this method is that it uses only acoustic images obtained from the side-scan sonar. This feature opens up the possibility of installing this technology in unmanned small digital devices. In this study, the classification accuracy of the proposed automation method is compared to the accuracy of traditional methods in order to show the usefulness of the technology. In addition, the proposed method is applied to real-world images of the seabed to evaluate its effectiveness in marine surveys. The thesis is organized as follows. In Chapter 1, the purpose of this study is presented and previous studies relevant to this research are reviewed. In Chapter 2, an overview of underwater sound is given and key principles of sound wave technology are explained. In Chapter 3, a new method for detecting and discriminating objects on the seafloor is proposed. In Chapter 4, the possibility of automating the discrimination method is explored. Finally, Chapter 5 summarizes the findings of this study and proposes new avenues for future research.九州工業大学博士学位論文 学位記番号:工博甲第364号　学位授与年月日:平成26年3月25日Chapter 1 Introduction|Chapter 2 Underwater acoustics|Chapter 3 Detection of underwater objects based on machine learning|Chapter 4 Automatic classification of seabed sediments using HLAC|Chapter 5 Conclusion九州工業大学平成25年

Ocean wind and wave parameter estimation from ship-borne x-band marine radar data

Ocean wind and wave parameters are important for the study of oceanography, on- and off-shore activities, and the safety of ship navigation. Conventionally, such parameters have been measured by in-situ sensors such as anemometers and buoys. During the last three decades, sea surface observation using X-band marine radar has drawn wide attention since marine radars can image both temporal and spatial variations of the sea surface. In this thesis, novel algorithms for wind and wave parameter retrieval from X-band marine radar data are developed and tested using radar, anemometer, and buoy data collected in a sea trial off the east coast of Canada in the North Atlantic Ocean. Rain affects radar backscatter and leads to less reliable wind parameters measurements. In this thesis, algorithms are developed to enable reliable wind parameters measurements under rain conditions. Firstly, wind directions are extracted from raincontaminated radar data using either a 1D or 2D ensemble empirical mode decomposition (EEMD) technique and are seen to compare favourably with an anemometer reference. Secondly, an algorithm based on EEMD and amplitude modulation (AM) analysis to retrieve wind direction and speed from both rain-free and rain-contaminated X-band marine radar images is developed and is shown to be an improvement over an earlier 1D spectral analysis-based method. For wave parameter measurements, an empirical modulation transfer function (MTF) is required for traditional spectral analysis-based techniques. Moreover, the widely used signal-to-noise ratio (SNR)-based method for significant wave height (HS) estimation may not always work well for a ship-borne X-band radar, and it requires external sensors for calibration. In this thesis, two methods are first presented for HS estimation from X-band marine radar data. One is an EEMD-based method, which enables satisfactory HS measurements obtained from a ship-borne radar. The other is a modified shadowingbased method, which enables HS measurements without the inclusion of external sensors. Furthermore, neither method requires the MTF. Finally, an algorithm based on the Radon transform is proposed to estimate wave direction and periods from X-band marine radar images with satisfactory results

Summary of Research 1994

The views expressed in this report are those of the authors and do not reflect the official policy or position of the Department of Defense or the U.S. Government.This report contains 359 summaries of research projects which were carried out under funding of the Naval Postgraduate School Research Program. A list of recent publications is also included which consists of conference presentations and publications, books, contributions to books, published journal papers, and technical reports. The research was conducted in the areas of Aeronautics and Astronautics, Computer Science, Electrical and Computer Engineering, Mathematics, Mechanical Engineering, Meteorology, National Security Affairs, Oceanography, Operations Research, Physics, and Systems Management. This also includes research by the Command, Control and Communications (C3) Academic Group, Electronic Warfare Academic Group, Space Systems Academic Group, and the Undersea Warfare Academic Group