Radar systems for a polar mission, volume 3, appendices A-D, S, T

Success is reported in the radar monitoring of such features of sea ice as concentration, floe size, leads and other water openings, drift, topographic features such as pressure ridges and hummocks, fractures, and a qualitative indication of age and thickness. Scatterometer measurements made north of Alaska show a good correlation with a scattering coefficient with apparent thickness as deduced from ice type analysis of stereo aerial photography. Indications are that frequencies from 9 GHz upward seem to be better for sea ice radar purposes than the information gathered at 0.4 GHz by a scatterometer. Some information indicates that 1 GHz is useful, but not as useful as higher frequencies. Either form of like-polarization can be used and it appears that cross-polarization may be more useful for thickness measurement. Resolution requirements have not been fully established, but most of the systems in use have had poorer resolution than 20 meters. The radar return from sea ice is found to be much different than that from lake ice. Methods to decrease side lobe levels of the Fresnel zone-plate processor and to decrease the memory requirements of a synthetic radar processor are discussed

Ship target recognition

Includes bibliographical references.In this report the classification of ship targets using a low resolution radar system is investigated. The thesis can be divided into two major parts. The first part summarizes research into the applications of neural networks to the low resolution non-cooperative ship target recognition problem. Three very different neural architectures are investigated and compared, namely; the Feedforward Network with Back-propagation, Kohonen's Supervised Learning Vector Quantization Network, and Simpson's Fuzzy Min-Max neural network. In all cases, pre-processing in the form of the Fourier-Modified Discrete Mellin Transform is used as a means of extracting feature vectors which are insensitive to the aspect angle of the radar. Classification tests are based on both simulated and real data. Classification accuracies of up to 93 are reported. The second part is of a purely investigative nature, and summarizes a body of research aimed at exploring new ground. The crux of this work is centered on the proposal to use synthetic range profiling in order to achieve a much higher range resolution (and hence better classification accuracies). Included in this work is a comprehensive investigation into the use of super-resolution and noise reducing eigendecomposition techniques. Algorithms investigated include the Principal Eigenvector Method, the Total Least Squares Method, and the MUSIC method. A final proposal for future research and development concerns the use of time domain averaging to improve the classification performance of the radar system. The use of an iterative correlation algorithm is investigated

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

Active microwave users working group program planning

A detailed programmatic and technical development plan for active microwave technology was examined in each of four user activities: (1) vegetation; (2) water resources and geologic applications, and (4) oceanographic applications. Major application areas were identified, and the impact of each application area in terms of social and economic gains were evaluated. The present state of knowledge of the applicability of active microwave remote sensing to each application area was summarized and its role relative to other remote sensing devices was examined. The analysis and data acquisition techniques needed to resolve the effects of interference factors were reviewed to establish an operational capability in each application area. Flow charts of accomplished and required activities in each application area that lead to operational capability were structured

Large-scale phenomena, chapter 3, part D

Oceanic phenomena with horizontal scales from approximately 100 km up to the widths of the oceans themselves are examined. Data include: shape of geoid, quasi-stationary anomalies due to spatial variations in sea density and steady current systems, and the time dependent variations due to tidal and meteorological forces and to varying currents

Examples of current radar technology and applications, chapter 5, part B

Basic principles and tradeoff considerations for SLAR are summarized. There are two fundamental types of SLAR sensors available to the remote sensing user: real aperture and synthetic aperture. The primary difference between the two types is that a synthetic aperture system is capable of significant improvements in target resolution but requires equally significant added complexity and cost. The advantages of real aperture SLAR include long range coverage, all-weather operation, in-flight processing and image viewing, and lower cost. The fundamental limitation of the real aperture approach is target resolution. Synthetic aperture processing is the most practical approach for remote sensing problems that require resolution higher than 30 to 40 m

Offshore oil spill detection using synthetic aperture radar

Among the different types of marine pollution, oil spill has been considered as a major threat to the sea ecosystems. The source of the oil pollution can be located on the mainland or directly at sea. The sources of oil pollution at sea are discharges coming from ships, offshore platforms or natural seepage from sea bed. Oil pollution from sea-based sources can be accidental or deliberate. Different sensors to detect and monitor oil spills could be onboard vessels, aircraft, or satellites. Vessels equipped with specialised radars, can detect oil at sea but they can cover a very limited area. One of the established ways to monitor sea-based oil pollution is the use of satellites equipped with Synthetic Aperture Radar (SAR).The aim of the work presented in this thesis is to identify optimum set of feature extracted parameters and implement methods at various stages for oil spill detection from Synthetic Aperture Radar (SAR) imagery. More than 200 images of ERS-2, ENVSAT and RADARSAT 2 SAR sensor have been used to assess proposed feature vector for oil spill detection methodology, which involves three stages: segmentation for dark spot detection, feature extraction and classification of feature vector. Unfortunately oil spill is not only the phenomenon that can create a dark spot in SAR imagery. There are several others meteorological and oceanographic and wind induced phenomena which may lead to a dark spot in SAR imagery. Therefore, these dark objects also appear similar to the dark spot due to oil spill and are called as look-alikes. These look-alikes thus cause difficulty in detecting oil spill spots as their primary characteristic similar to oil spill spots. To get over this difficulty, feature extraction becomes important; a stage which may involve selection of appropriate feature extraction parameters. The main objective of this dissertation is to identify the optimum feature vector in order to segregate oil spill and ‘look-alike’ spots. A total of 44 Feature extracted parameters have been studied. For segmentation, four methods; based on edge detection, adaptive theresholding, artificial neural network (ANN) segmentation and the other on contrast split segmentation have been implemented. Spot features are extracted from both the dark spots themselves and their surroundings. Classification stage was performed using two different classification techniques, first one is based on ANN and the other based on a two-stage processing that combines classification tree analysis and fuzzy logic. A modified feature vector, including both new and improved features, is suggested for better description of different types of dark spots. An ANN classifier using full spectrum of feature parameters has also been developed and evaluated. The implemented methodology appears promising in detecting dark spots and discriminating oil spills from look-alikes and processing time is well below any operational service requirements

Investigation of ship target recognition using neural networks in conjunction with the Fourier Mellin transform

The purpose of this dissertation is to investigate the feasibility of using neural networks in conjunction with the Fourier Modified Direct Mellin Transform (FMDMT) for the recognition of ship targets. The FMDMT is a modification of the Direct Mellin Transform for digital implementations, and is applied to the magnitudes of the Discrete Fourier Transforms (DFT) of range profiles of ships. Necessity for the use of the FMDMT is corroborated by the fact that features can be extracted from the range profiles of targets, regardless of target aspect angle. Variation in aspect angle results in variation of the independent variable. Feature extraction is made possible by the scale invariant properties of the Mellin Transform. Substantial emphasis was placed on preprocessing techniques applied in the implementation of the FMDMT on simulated range profiles and in particular, real ship profiles. The FMDMT was thus examined extensively and utilised as it was developed and demonstrated in [20]. At the completion of this examination, the recognition procedures and methods were applied on simulated data with the aid of a radar simulator developed and adapted for this dissertation. Results of the recognition of simulated ship targets were scrutinized closely and recorded. Employment of this procedure afforded the ability to compare the recognition results for real ship data with those of simulated ship data at a later stage. Acquisition of a large database of ship profiles was made successful by a ship target data capture plan implemented at the Institute for Maritime Technology (IMT) in Simon's Town. The database included the radar range profile data for the SAS Protea and the Outeniqua, which carried out several successful full circular manoeuvres in the line of sight of the search radar utilised (Raytheon). The relevant ships performed these circular manoeuvres in order that the acquired data incorporate radar range profiles of the relevant ships at most aspect angles from 0 degrees to 360 degrees. Extensive and thorough testing of the performance of the FMDMT would thus be possible since every possible aspect angle would be scrutinized. Preprocessing of data and recognition of targets was implemented in exactly the same manner and order as was the case with the simulated ship data. Extensive examination of the FMDMT revealed that the MDMT should only be applied to one side of a real and even Fourier Transform of a ship target. Literature on the FMDMT had failed to elaborate on this point. Comparison of the recognition results for real and simulated data, indicates a great similarity in success, thus validating the methods and procedures described theoretically and adopted practically for preprocessing of the radar range profiles and recognition of the targets. In order to demonstrate the feasibility of ship target recognition using the procedures and methods incorporated in the dissertation, real ship data for an entire range of different ships should be acquired in the same manner as indicated above. Bibliography: pages 117-118

An introduction to radar Automatic Target Recognition (ATR) technology in ground-based radar systems

This paper presents a brief examination of Automatic Target Recognition (ATR) technology within ground-based radar systems. It offers a lucid comprehension of the ATR concept, delves into its historical milestones, and categorizes ATR methods according to different scattering regions. By incorporating ATR solutions into radar systems, this study demonstrates the expansion of radar detection ranges and the enhancement of tracking capabilities, leading to superior situational awareness. Drawing insights from the Russo-Ukrainian War, the paper highlights three pressing radar applications that urgently necessitate ATR technology: detecting stealth aircraft, countering small drones, and implementing anti-jamming measures. Anticipating the next wave of radar ATR research, the study predicts a surge in cognitive radar and machine learning (ML)-driven algorithms. These emerging methodologies aspire to confront challenges associated with system adaptation, real-time recognition, and environmental adaptability. Ultimately, ATR stands poised to revolutionize conventional radar systems, ushering in an era of 4D sensing capabilities