Evaluation and improvement of methods for estimating sea surface wave parameters from X-band marine radar data

In this thesis, several algorithms have been proposed for estimating ocean wave parameters from X-band marine radar data, i.e., wave direction, wave period, and significant wave height. In the first part of this study, the accuracy of wave direction and period estimation from X-band marine radar images under different rain rates is analyzed, and a sub-image selection scheme is proposed to mitigate the rain effect. Firstly, each radar image is divided into multiple sub-images, and the sub-images with relatively clear wave signatures are identified based on a random-forest based classiffication model. Then, wave direction is estimated by performing a Radon transform (RT) on each valid sub-image. As for wave period estimation, a random-forest based regression method is proposed. Texture features are first extracted from each pixel of the selected sub-image using the gray-level co-occurrence matrix (GLCM) and combined as a feature vector. Those feature vectors extracted from both rain-free and rain-contaminated training samples are then used to train a random-forest based wave period regression model. Shore-based X-band marine radar images, simultaneous rain rate data, as well as buoy-measured wave data collected on the West Coast of the United States are used to analyze the rain effect on wave parameter estimation accuracy and to validate the proposed method. Experimental results show that the proposed subimage selection scheme improves the estimation accuracy of both wave direction and wave period under different rain rates, with reductions of root-mean-square errors (RMSEs) by 6.9ﾟ, 6.0ﾟ, 4.9ﾟ, and 1.0ﾟ for wave direction under rainless, light rain, moderate rain, and heavy rain conditions, respectively. As for wave period estimation, the RMSEs decrease by 0.13 s, 0.20 s, 0.30 s, and 0.20 s under those four rainfall intensity levels, respectively. The second part of research focuses on the estimation of significant wave height (Hₛ). A temporal convolutional network (TCN)-based model is proposed to retrieve Hₛ from X-band marine radar image sequences. Three types of features are first extracted from radar image sequences based on signal to noise ratio (SNR), ensemble empirical mode decomposition (EEMD), and GLCM methods, respectively. Then, feature vectors are input into the proposed TCN-based regression model to produce Hₛ estimation. Radar data are collected from a moving vessel at the East Coast of Canada, as well as simultaneously collected wave data from several wave buoys deployed nearby are used for model training and testing. After averaging, experimental results show that the TCN-based model further improves the Hₛ estimation accuracy, with reductions of RMSEs by 0.33 m and 0.10 m, respectively, compared to the SNR-based and the EEMD-based linear fitting methods. It has also been found that with the same feature extraction scheme, TCN outperforms other machine-learning based algorithms including support vector regression (SVR) and the convolutional gated recurrent unit (CGRU) network

A Novel Scheme for Extracting Sea Surface Wind Information From Rain-Contaminated X-Band Marine Radar Images

The presence of rain degrades the performance of sea surface parameter estimation using X-band marine radar. In this article, a novel scheme is proposed to improve wind measurement accuracy from rain-contaminated X-band marine radar data. After extracting texture features from each image pixel, the rain-contaminated regions with blurry wave signatures are first identified using a self-organizing map (SOM)-based clustering model. Then, a convolutional neural network used for image haze removal, i.e., DehazeNet is introduced and incorporated into the proposed scheme for correcting the influence of rain on radar images. In order to obtain wind direction information, curve fitting is conducted on the average azimuthal intensities of rain-corrected radar images. On the other hand, wind speed is estimated from rain-corrected images by training a support vector regression-based model. Experiments conducted using datasets from both shipborne and onshore marine radar show that compared to results obtained from images without rain correction, the proposed method achieves relatively high estimation accuracy by reducing measurement errors significantly

Evaluation and Mitigation of Rain Effect on Wave Direction and Period Estimation From X-Band Marine Radar Images

In this article, the accuracy of wave direction and period estimation from X-band marine radar images under different rain rates is analyzed, and a simple subimage selection scheme is proposed to mitigate the rain effect. First, each radar image is divided into multiple subimages, and the subimages with relatively clear wave signatures are identified based on the random-forest-based classification model. Then, wave direction is estimated by performing the Radon transform on each valid subimage. As for wave period estimation, a new method is proposed. Texture features are first extracted from each pixel of the selected subimage using the gray-level co-occurrence matrix and combined as a feature vector. Those feature vectors extracted from both rain-free and rain-contaminated training samples are then used to train a random-forest-based wave period regression model. The shore-based X-band marine radar images, simultaneous rain rate data, as well as buoy-measured wave data collected on the West Coast of the United States are used to analyze the rain effect on wave parameter estimation accuracy and validate the proposed method. Experimental results show that the proposed subimage selection scheme improves the estimation accuracy of both wave direction and wave period under different rain rates, with reductions of root-mean-square errors (RMSEs) by 6.9 ° , 6.0 ° , 4.9 ° , and 1.0 ° for wave direction under rainless, light rain, moderate rain, and heavy rain conditions, respectively. As for wave period estimation, the RMSEs decrease by 0.13, 0.20, 0.30, and 0.20 s under those four rainfall intensity levels, respectively

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

Sea surface wind and wave parameter estimation from X-band marine radar images with rain detection and mitigation

In this research, the application of X-band marine radar backscatter images for sea surface wind and wave parameter estimation with rain detection and mitigation is investigated. In the presence of rain, the rain echoes in the radar image blur the wave signatures and negatively affect estimation accuracy. Hence, in order to improve estimation accuracy, it is meaningful to detect the presence of those rain echoes and mitigate their influence on estimation results. Since rain alters radar backscatter intensity distribution, features are extracted from the normalized histogram of each radar image. Then, a support vector machine (SVM)-based rain detection model is proposed to classify radar images obtained between rainless and rainy conditions. The classification accuracy shows significant improvement compared to the existing threshold-based method. By further observing images obtained under rainy conditions, it is found that many of them are only partially contaminated by rain echoes. Therefore, in order to segment between rain-contaminated regions and those that are less or unaffected by rain, two types of methods are developed based on unsupervised learning techniques and convolutional neural network (CNN), respectively. Specifically, for the unsupervised learning-based method, texture features are first extracted from each pixel and then trained using a self organizing map (SOM)-based clustering model, which is able to conduct pixel-based identification of rain-contaminated regions. As for the CNN-based method, a SegNet-based semantic segmentation CNN is �rst designed and then trained using images with manually annotated labels. Both shipborne and shore-based marine radar data are used to train and validate the proposed methods and high classification accuracies of around 90% are obtained. Due to the similarities between how haze affects terrestrial images and how rain affects marine radar images, a type of CNN for image dehazing purposes, i.e., DehazeNet, is applied to rain-contaminated regions in radar images for correcting the in uence of rain, which reduces the estimation error of wind direction significantly. Besides, after extracting histogram and texture features from rain-corrected radar images, a support vector regression (SVR)-based model, which achieves high estimation accuracy, is trained for wind speed estimation. Finally, a convolutional gated recurrent unit (CGRU) network is designed and trained for significant wave height (SWH) estimation. As an end-to-end system, the proposed network is able to generate estimation results directly from radar image sequences by extracting multi-scale spatial and temporal features in radar image sequences automatically. Compared to the classic signal-to-noise (SNR)-based method, the CGRU-based model shows significant improvement in both estimation accuracy (under both rainless and rainy conditions) and computational efficiency

Remote sensing in the coastal and marine environment. Proceedings of the US North Atlantic Regional Workshop

Presentations were grouped in the following categories: (1) a technical orientation of Earth resources remote sensing including data sources and processing; (2) a review of the present status of remote sensing technology applicable to the coastal and marine environment; (3) a description of data and information needs of selected coastal and marine activities; and (4) an outline of plans for marine monitoring systems for the east coast and a concept for an east coast remote sensing facility. Also discussed were user needs and remote sensing potentials in the areas of coastal processes and management, commercial and recreational fisheries, and marine physical processes

OIL SPILL ALONG THE TURKISH STRAITS SEA AREA; ACCIDENTS, ENVIRONMENTAL POLLUTION, SOCIO-ECONOMIC IMPACTS AND PROTECTION

The Turkish Straits Sea Area (TSSA) is a long water passage that is consisted of the Sea of Marmara, an inland sea within Turkey's borders, and two narrow straits connected to neighboring seas. With a strategic location between the Balkans and Anatolia, the Black Sea and the Mediterranean, and dominated by the continental climate, the region hosted many civilizations throughout the centuries. This makes the region among the busiest routes in the world, with sea traffic three times higher than that in the Suez Canal. The straits are the most difficult waterways to navigate and witnessed many hazardous and important collisions and accidents throughout history. In addition, this area has vital roles as a biological corridor and barrier among three distinctive marine realms. Therefore, the region is rather sensitive to damages of national and international maritime activities, which may cause severe environmental problems. This book addresses several key questions on a chapter basis, including historical accidents, background information on main dynamic restrictions, oil pollution, oil spill detection, and clean-up recoveries, its impacts on biological communities, socioeconomic aspects, and subjects with international agreements. This book will help readers, public, local and governmental authorities gain a deeper understanding of the status of the oil spill, mostly due to shipping accidents, and their related impacts along the TSSA, which needs precautionary measures to be protected.CONTENTS INTRODUCTION CHAPTER I - HISTORY OF ACCIDENTS AND REGULATIONS Remarkable Accidents at the Istanbul Strait Hasan Bora USLUER and Saim OĞUZÜLGEN …………………………………...... 3 History of Regulations before Republican Era along the Turkish Straits Sea Area Ali Umut ÜNAL …………………………………………………………………….. 16 Transition Regime in the Turkish Straits during the Republican Era Osman ARSLAN ……….……………………………………………………….……26 26 The Montreux Convention and Effects at Turkish Straits Oktay ÇETİN ………………………………………………………………….…….. 33 Evaluation of the Montreux Convention in the Light of Recent Problems Ayşenur TÜTÜNCÜ ………………………………………………………………… 44 A Historical View on Technical Developments on Ships and Effects of Turkish Straits Murat YAPICI ………………………………………………………………………. 55 CHAPTER II - GEOGRAPHY, BATHYMETRY AND HYDRO-METEOROLOGICAL CONDITIONS Geographic and Bathymetric Restrictions along the Turkish Straits Sea Area Bedri ALPAR, Hasan Bora USLUER and Şenol AYDIN ……………………..…… 61 Hydrodynamics and Modeling of Turkish Straits Serdar BEJİ and Tarkan ERDİK ………………………………………………….… 79 Wave Climate in the Turkish Sea of Marmara Tarkan ERDİK and Serdar BEJİ …………………………………………………..… 91 CHAPTER III - OIL POLLUTION, DETECTION AND RECOVERY Oil Pollution at Sea and Coast Following Major Accidents Selma ÜNLÜ ……………………………………………………………………….101 Forensic Fingerprinting in Oil-spill Source Identification at the Turkish Straits Sea Area Özlem ATEŞ DURU ……………………………………………………………… 121 xi Oil Spill Detection Using Remote Sensing Technologies-Synthetic Aperture Radar (SAR) İbrahim PAPİLA, Elif SERTEL, Şinasi KAYA and Cem GAZİOĞLU ……..……. 140 The Role of SAR Remote Sensing to Detect Oil Pollution and Emergency Intervention Saygın ABDIKAN, Çağlar BAYIK and Füsun BALIK ŞANLI ……….….……….. 157 Oil Spill Recovery and Clean-Up Techniques Emra KIZILAY, Mehtap AKBAŞ and Tahir Yavuz GEZBELİ …………………… 176 Turkish Strait Sea Area, Contingency Planning, Regulations and Case Studies Emra KIZILAY, Mehtap AKBAŞ and Tahir Yavuz GEZBELİ …………………... 188 Dispersant Response Method to Incidental Oil Pollution Dilek EDİGER, Leyla TOLUN and Fatma TELLİ KARAKOÇ ………………….... 205 CHAPTER IV - THE EFFECTS / IMPACTS OF OIL SPILL ON BIOLOGICAL COMMUNITIES – INCLUDING SAMPLING AND MONITORING Marine Microorganisms and Oil Spill Sibel ZEKİ and Pelin S. ÇİFTÇİ TÜRETKEN …………...………………………… 219 Estimated Effects of Oil Spill on the Phytoplankton Following “Volgoneft-248” Accident (Sea of Marmara) Seyfettin TAŞ ………………………………..…………………………………….... 229 Interactions between Zooplankton and Oil Spills: Lessons Learned from Global Accidents and a Proposal for Zooplankton Monitoring İ. Noyan YILMAZ and Melek İŞİNİBİLİR ……………………………………..….. 238 The Effects of Oil Spill on the Macrophytobenthic Communities Ergün TAŞKIN and Barış AKÇALI …………………………….…………….……. 244 Potential Impacts of Oil Spills on Macrozoobenthos in the Turkish Straits System Güley KURT-ŞAHİN …………………………………………………………….… 253 The Anticipated Effects of Oil Spill on Fish Populations in Case of an Accident along the Turkish Straits System – A review of Studies after Several Incidents from the World M. İdil ÖZ and Nazlı DEMİREL …………………………………………………….261 Estimated Impacts of an Oil Spill on Bird Populations along the Turkish Straits System Itri Levent ERKOL …………………………………………………………….…… 272 The Effect of Oil Spills on Cetaceans in the Turkish Straits System (TSS) Ayaka Amaha ÖZTÜRK ………………………………………………………….. 277 Changes in the Ichthyoplankton and Benthos Assemblages following Volgoneft-248 Oil Spill: Case Study Ahsen YÜKSEK and Yaprak GÜRKAN …………………………………….……. 280 Assessing the Initial and Temporal Effects of a Heavy Fuel Oil Spill on Benthic Fauna Yaprak GÜRKAN, Ahsen YÜKSEK ………………………………………..…….. 287 CHAPTER V - SOCIO-ECONOMIC ASPECTS Socio-economic Aspects of Oil Spill Özlem ATEŞ DURU and Serap İNCAZ ……………………………………….…… 301 Effects of Oil Spill on Human Health Türkan YURDUN ………………………………………………………………..…. 313 Crisis Management of Oil Spill, A Case Study: BP Gulf Mexico Oil Disaster Serap İNCAZ and Özlem ATEŞ DURU …………………………….………….……324 CHAPTER VI - CONVENTIONS RELATING TO PREVENTION OF OIL SPILL International Convention for the Prevention of Pollution of the Sea by Oil (OILPOL), 1954 and its Situation Related with Turkey Emre AKYÜZ, Metin ÇELİK and Ömer SÖNER …………………………...……... 334 International Convention for the Prevention of Pollution from Ships, 1973, as Modified by the Protocol of 1978 Relating Thereto and by the Protocol of 1997 (MARPOL) Özcan ARSLAN, Esma UFLAZ and Serap İNCAZ ………………………….……. 342 Applications of MARPOL Related with Oil Spill in Turkey Emre AKYÜZ, Özcan ASLAN and Serap İNCAZ ………………………………… 356 Ship Born Oil Pollution at the Turkish Straits Sea Area and MARPOL 73/78 Duygu ÜLKER and Sencer BALTAOĞLU………………………….…………….. 363 International Convention Relating to Intervention on the High Seas in Cases of Oil Pollution Casualties (INTERVENTION 1969) and its Applications Related with Oil Spill in Turkey Şebnem ERKEBAY ……………………………….……………………………….. 371 International Convention on Oil Pollution Preparedness, Response and Co-operation (OPRC) 1990 and its Applications Related with Oil Spill in Turkey Kadir ÇİÇEK ………………………………………………………………………. 381 Protocol on Preparedness, Response and Co-operation to Pollution Incidents by Hazardous and Noxious Substances, 2000 (OPRC-HNS Protocol) and its Effects in Turkey Aydın ŞIHMANTEPE and Cihat AŞAN ……………….…………………………. 392 The International Convention on Salvage (SALVAGE) 1989 Related with Oil Spill in Turkey İrşad BAYIRHAN ……………………………………….………………..……….. 408 CHAPTER VII - CONVENTIONS COVERING LIABILITY AND COMPENSATION RELATED WITH OIL SPILL International Convention on Civil Liability for Oil Pollution Damage (CLC), 1969 and its Applications Serap İNCAZ and Pınar ÖZDEMİR ……………………………………..………… 416 1992 Protocol to the International Convention on the Establishment of an International Fund for Compensation for Oil Pollution Damage (FUND 1992) and its Applications Related with Oil Spill in Turkey Ali Umut ÜNAL and Hasan Bora USLUER …………………………….………… 424 International Convention on Liability and Compensation for Damage in Connection with the Carriage of Hazardous and Noxious Substances by Sea (HNS), 1996 (and its 2010 Protocol) and its Applications Related with Oil Spill in Turkey Bilun ELMACIOĞLU ……………………………………………………………… 437 Bunkering Incidents and Safety Practices in Turkey Fırat BOLAT, Pelin BOLAT and Serap İNCAZ …………………………………... 447 "Nairobi International Convention on the Removal of Wrecks 2007" and its Effects on Turkey Şafak Ümit DENİZ and Serap İNCAZ ……………………….……………………. 457

GIS based models for optimisation of marine cage aquaculture in Tenerife, Canary Islands

This study focused on the optimisation of offshore marine fish-cage farming in Tenerife, Canary Islands. The main objective was to select the most suitable sites for offshore cage culture. This is a key factor in any aquaculture operation, affecting both success and sustainability. Moreover, it can solve conflicts between different coastal activities, making a rational use of the coastal space. Site selection was achieved by using Geographical Information Systems (GIS) based models and related technology, such as satellite images and Global Positioning System (GPS), to support the decision-making process. Three different cage systems were selected and proposed for different areas around Tenerife. Finally, a particulate waste distribution model (uneaten feed and faeces) was developed, also using GIS, for future prediction of the dispersive nature of selected sites. This can reduce the number of sites previously identified as most suitable, by predicting possible environmental impacts on the benthos if aquaculture was to be developed on a specific site. The framework for spatial multi-criteria decision analysis used in this study began with a recognition and definition of the decision problem. Subsequently, 31 production functions (factors and constraints) were identified, defined and subdivided into 8 sub-models. These sub-models were then integrated into a GIS database in the form of thematic layers and later scored for standardization. At this stage, the database was verified by field sampling to establish the quality of data used. The decision maker's preferences were incorporated into the decision model by assigning weights of relative importance to the evaluation under consideration. These, together with the thematic layers, were integrated by using Multi-criteria Evaluation (MCE) and simple overlays to provide an overall assessment of possible alternatives. Finally, sensitivity analysis was performed to determine the model robustness. The integration, manipulations and presentation of the results by means of GIS-based models in this sequential and logical flow of steps proved to be very effective for helping the decision-making process of site selection in study. On the whole, this study revealed the usefulness of GIS as an aquaculture planning and management tool. Cage systems that can withstand harsh environments were found to be suitable for use over a broader area of Tenerife's coastline. Thus, the more robust self-tensioned cage (SeaStation®) could be used over a greater area than the weaker gravity cages (Corelsa®). From the 228 km2 of available area for siting cages in the coastal regions with depth of 50 m, the suitable area (sum of scores 6, 7 and 8) for siting SeaStation® cages was 61 km2, while the suitable area for SeaStation® and Corelsa® cages was 49 and 37 km2 respectively. Most of the variation between these three cage systems was found among the intermediate suitability scores. It was concluded that the biggest differences in suitable area among cage systems are between Corelsa® and SeaStation® systems, followed by differences between Corelsa® and OceanSpar® cages, and OceanSpar® and SeaStation® respectively. This variability was mostly located on the N and NNW of the island, where waves, both long and short-term, are higher

Maritime Moving Target Detection, Tracking and Geocoding Using Range-Compressed Airborne Radar Data

Eine regelmäßige und großflächige überwachung des Schiffsverkehrs gewinnt zunehmend an Bedeutung, vor allem auch um maritime Gefahrenlagen und illegale Aktivitäten rechtzeitig zu erkennen. Heutzutage werden dafür überwiegend das automatische Identifikationssystem (AIS) und stationäre Radarstationen an den Küsten eingesetzt. Luft- und weltraumgestützte Radarsensoren, die unabhängig vom Wetter und Tageslicht Daten liefern, können die vorgenannten Systeme sehr gut ergänzen. So können sie beispielsweise Schiffe detektieren, die nicht mit AIS-Transpondern ausgestattet sind oder die sich außerhalb der Reichweite der stationären AIS- und Radarstationen befinden. Luftgestützte Radarsensoren ermöglichen eine quasi-kontinuierliche Beobachtung von räumlich begrenzten Gebieten. Im Gegensatz dazu bieten weltraumgestützte Radare eine große räumliche Abdeckung, haben aber den Nachteil einer geringeren temporalen Abdeckung. In dieser Dissertation wird ein umfassendes Konzept für die Verarbeitung von Radardaten für die Schiffsverkehr-überwachung mit luftgestützten Radarsensoren vorgestellt. Die Hauptkomponenten dieses Konzepts sind die Detektion, das Tracking, die Geokodierung, die Bildgebung und die Fusion mit AIS-Daten. Im Rahmen der Dissertation wurden neuartige Algorithmen für die ersten drei Komponenten entwickelt. Die Algorithmen sind so aufgebaut, dass sie sich prinzipiell für zukünftige Echtzeitanwendungen eignen, die eine Verarbeitung an Bord der Radarplattform erfordern. Darüber hinaus eignen sich die Algorithmen auch für beliebige, nicht-lineare Flugpfade der Radarplattform. Sie sind auch robust gegenüber Lagewinkeländerungen, die während der Datenerfassung aufgrund von Luftturbulenzen jederzeit auftreten können. Die für die Untersuchungen verwendeten Daten sind ausschließlich entfernungskomprimierte Radardaten. Da das Signal-Rausch-Verhältnis von Flugzeugradar-Daten im Allgemeinen sehr hoch ist, benötigen die neuentwickelten Algorithmen keine vollständig fokussierten Radarbilder. Dies reduziert die Gesamtverarbeitungszeit erheblich und ebnet den Weg für zukünftige Echtzeitanwendungen. Der entwickelte neuartige Schiffsdetektor arbeitet direkt im Entfernungs-Doppler-Bereich mit sehr kurzen kohärenten Verarbeitungsintervallen (CPIs) der entfernungskomprimierten Radardaten. Aufgrund der sehr kurzen CPIs werden die detektierten Ziele im Dopplerbereich fokussiert abgebildet. Wenn sich die Schiffe zusätzlich mit einer bestimmten Radialgeschwindigkeit bewegen, werden ihre Signale aus dem Clutter-Bereich hinausgeschoben. Dies erhöht das Verhältnis von Signal- zu Clutter-Energie und verbessert somit die Detektierbarkeit. Die Genauigkeit der Detektion hängt stark von der Qualität der von der Meeresoberfläche rückgestreuten Radardaten ab, die für die Schätzung der Clutter-Statistik verwendet werden. Diese wird benötigt, um einen Detektions-Schwellenwert für eine konstante Fehlalarmrate (CFAR) abzuleiten und die Anzahl der Fehlalarme niedrig zu halten. Daher umfasst der vorgeschlagene Detektor auch eine neuartige Methode zur automatischen Extraktion von Trainingsdaten für die Statistikschätzung sowie geeignete Ozean-Clutter-Modelle. Da es sich bei Schiffen um ausgedehnte Ziele handelt, die in hochauflösenden Radardaten mehr als eine Auflösungszelle belegen, werden nach der Detektion mehrere von einem Ziel stammende Pixel zu einem physischen Objekten zusammengefasst, das dann in aufeinanderfolgenden CPIs mit Hilfe eines Bewegungsmodells und eines neuen Mehrzielverfolgungs-Algorithmus (Multi-Target Tracking) getrackt wird. Während des Trackings werden falsche Zielspuren und Geisterzielspuren automatisch erkannt und durch ein leistungsfähiges datenbankbasiertes Track-Management-System terminiert. Die Zielspuren im Entfernungs-Doppler-Bereich werden geokodiert bzw. auf den Boden projiziert, nachdem die Einfallswinkel (DOA) aller Track-Punkte geschätzt wurden. Es werden verschiedene Methoden zur Schätzung der DOA-Winkel für ausgedehnte Ziele vorgeschlagen und anhand von echten Radardaten, die Signale von echten Schiffen beinhalten, bewertet

Earth Resources: A continuing bibliography with indexes, issue 33

This bibliography list 436 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution sytems, instrumentation and sensors, and economic analysis