Matthew Lybanon

Stennis Space Center

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Suzanne M. Lea

University of North Carolina at Greensboro

Ronald J. Holyer

Stennis Space Center

Kenneth Messa

Loyola University New Orleans

Ron Holyer

Stennis Space Center

N. KRISHNAKUMAR

Twin Cities Orthopedics

S. S. Iyengar

Florida International University

Sarah H. Peckinpaugh

Stennis Space Center

Bill P. Buckles

University of North Texas

J.H. Allender

Chevron (United States)

Larry K. Gronmeyer

Cooperative Institutions

Louisiana State University

Stennis Space Center

United States Naval Research Laboratory

Indian Institute of Science Bangalore

System Science Applications (United States)

Florida International University

NOAA Oceanic and Atmospheric Research

Tulane University

Systems Planning and Analysis (United States)

University of Tennessee at Knoxville

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A better least-squares method when both variables have uncertainties

American Journal of Physics (1984)

Matthew Lybanon

The generalized least-squares problem, in which an observation vector satisfies a set of equations that may be nonlinear and implicit, and all components may be subject to errors, can be solved as a constrained minimization problem. When the analysis is specialized to the important case of one-dimensional curve fitting to measurements where both variables contain errors, it becomes similar to the effective variance method. A standard least-squares computer program can be used to apply the new method; the results are superior to these of the effective variance technique. A simple geometrical construction illustrates the principles of the new method.

Least-squares function approximation

Minification

10.1119/1.13822

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Citations (111)

Application of a discrete nonlinear spectral model to ideal cases of wind wave generation

NASA STI/Recon Technical Report N (1982)

J.H. Allender Matthew Lybanon

Abstract : The discrete nonlinear spectral model developed by Allender, Barnett, and Lybanon is applied to seven ideal cases of wind wave generation. These cases form the basis of an international study to compare wave prediction models. Model results are summarized graphically for each case, as applicable, and a brief explanation of model behavior is given. The model is free of numberical dispersion and damping, and reproduces the JONSWAP relations for fetch- and duration-limited wave growth quite well. Model results from cases with increasing complexity provide a benchmark for understanding model behavior in actual situations and for future improvements. (Author)

Fetch

Wave model

Benchmark (surveying)

Nonlinear model

Basis (linear algebra)

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Finding mesoscale ocean structures with mathematical morphology

Remote Sensing of Environment (1993)

Suzanne M. Lea Matthew Lybanon

Morphology

10.1016/0034-4257(93)90100-c

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Citations (9)

Predicting and explaining the movement of mesoscale oceanographic features using CLIPS

Susan M. Bridges Liang-Chun Chen Matthew Lybanon

The Naval Research Laboratory has developed an oceanographic expert system that describes the evolution of mesoscale features in the Gulf Stream region of the northwest Atlantic Ocean. These features include the Gulf Stream current and the warm and cold core eddies associated with the Gulf Stream. An explanation capability was added to the eddy prediction component of the expert system in order to allow the system to justify the reasoning process it uses to make predictions. The eddy prediction and explanation components of the system have recently been redesigned and translated from OPS83 to C and CLIPS and the new system is called WATE (Where Are Those Eddies). The new design has improved the system's readability, understandability and maintainability and will also allow the system to be incorporated into the Semi-Automated Mesoscale Analysis System which will eventually be embedded into the Navy's Tactical Environmental Support System, Third Generation, TESS(3).

Gulf Stream

Component (thermodynamics)

Maintainability

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An Expert System For Interpreting Mesoscale Features In Oceanographic Satellite Images

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (1989)

N. KRISHNAKUMAR S. S. Iyengar Ron Holyer Matthew Lybanon

Thermal infrared images of the ocean obtained from satellite sensors are widely used for the study of ocean dynamics. The derivation of mesoscale ocean information from satellite data depends to a large extent on the correct interpretation of infrared oceanographic images. The difficulty of the image analysis and understanding problem for oceanographic images is due in large part to the lack of precise mathematical descriptions of the ocean features, coupled with the time varying nature of these features and the complication that the view of the ocean surface is typically obscured by clouds, sometimes almost completely. Towards this objective, the present paper describes a hybrid technique that utilizes a nonlinear probabilistic relaxation method and an expert system for the oceanographic image interpretation problem. A unified mathematical framework that helps in solving the problem is presented. This paper highlights the advantages of using the contextual information in the feature labeling algorithm. The paper emphasizes the need for the feedback from the high level modules to the intermediate modules in an automatic image interpretation system. The paper presents some important results of the series of experiments conducted at Remote Sensing Branch, NORDA, on the NOAA AVHRR imagery data. Key words: feature labeling, feature extraction, oceanic features, edge detection, knowledge based systems, expert system, relaxation, infrared imagery.

Feature (linguistics)

10.1117/12.969270

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Citations (3)

<title>Morphological edge detection for oceanographic images</title>

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (1994)

Shantha Krishnamurthy S. S. Iyengar Ronald J. Holyer Matthew Lybanon

Mesoscale oceanographic features are important aspects of ocean circulation. The high volume of satellite-derived oceanographic data coupled with the high level of human skill associated with the detection of oceanographic features in the data has necessitated automating the interpretation process. Morphological edge detectors produce better results than the conventional template- and differentiation-based edge detectors. A grayscale morphological edge-detection algorithms is developed for automatic delineation of mesoscale structure in digital satellite IR images of the ocean. We compare performances of three morphological edge detectors in sea surface temperature fields. We provide experimental results on images from the North Atlantic under various image settings.

10.1117/12.169465

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Citations (2)

Maltese Front variability from satellite observations based on automated-detection

Matthew Lybanon

Maltese Front location statistics are obtained from multi-channel sea-surface temperature (MCSST) images, derived from Advanced Very High Resolution Radiometer (AVHRR) observations of the Mediterranean Sea during 3 March-27 September 1993. The statistics are based on semi-automated determinations of the front's SST surface expression. Expert analyses from-the Naval Oceanographic Office give an accuracy check. Expert techniques are largely manual, labor-intensive, subjective, and skill-dependent; automation could be beneficial. A mathematical morphology-based method successfully delineates the front; it finds the temperature gradients most likely to be the front's and presents the corresponding segmentations to the operator. The method was developed to find stars in astronomical images, and has successfully analyzed solar magnetograms and satellite Gulf Stream images. Rings, fronts, and sunspots are not star-like, but simple pre-processing adapts the technique to these problem domains. This work constitutes another successful application. The success in "moving" the technique is encouraging. The Maltese Front's thermal gradients are 5-10 times weaker than the Gulf Stream North Wall's, yet the method produces useful results. So, it may work in other regions.

Maltese

Gulf Stream

10.1109/igarss.1995.520276

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Feature labelling in infrared oceanographic images

Image and Vision Computing (1990)

N. KRISHNAKUMAR S. Sitharama lyengar Ron Holyer Matthew Lybanon

Feature (linguistics)

10.1016/0262-8856(90)90029-5

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Citations (8)