Matthew Lybanon

Wavelet-based feature extraction from oceanographic images

Features in satellite images of the oceans often have weak edges. These images also have a significant amount of noise, which is either due to the clouds or atmospheric humidity. The presence of noise compounds the problems associated with the detection of features, as the use of any traditional noise removal technique will also result in the removal of weak edges. Recently, there have been rapid advances in image processing as a result of the development of the mathematical theory of wavelet transforms. This theory led to multifrequency channel decomposition of images, which further led to the evolution of important algorithms for the reconstruction of images at various resolutions from the decompositions. The possibility of analyzing images at various resolutions can be useful not only in the suppression of noise, but also in the detection of fine features and their classification. This paper presents a new computational scheme based on multiresolution decomposition for extracting the features of interest from the oceanographic images by suppressing the noise. The multiresolution analysis from the median presented by Starck-Murtagh-Bijaoui (1994) is used for the noise suppression.

Histogram-based morphological edge detector

Presents a new edge detector for automatic extraction of oceanographic (mesoscale) features present in infrared (IR) images obtained from the Advanced Very High Resolution Radiometer (AVHRR). Conventional edge detectors are very sensitive to edge fine structure, which makes it difficult to distinguish the weak gradients that are useful in this application from noise. Mathematical morphology has been used in the past to develop efficient and statistically robust edge detectors. Image analysis techniques use the histogram for operations such as thresholding and edge extraction in a local neighborhood in the image. An efficient computational framework is discussed for extraction of mesoscale features present in IR images. The technique presented in the present article, called the Histogram-Based Morphological Edge detector (HMED), extracts all the weak gradients, yet retains the edge sharpness in the image. A new morphological operation defined in the domain of the histogram of an image is also presented. An interesting experimental result was found by applying the HMED technique to oceanographic data in which certain features are known to have edge gradients of varying strength. >

Automated boundary delineation in infrared ocean images

A mathematical morphology technique aids in interpreting satellite infrared images of the Gulf Stream region. The method delineates the North Wall clearly, with less smoothing than human analysts. Performance in locating warm-core eddies is comparable to that of humans and another automated procedure. A variation of the method finds cold-core eddies. >

Segmentation of diverse image types using opening and closing

A flexible approach to image segmentation, originally developed for optical astronomical images, is useful for analyzing infrared oceanographic images and solar magnetograms; it should be applicable in other problem domains. The technique is based on the morphological operations of opening and closing. The original computer implementation was designed to find star boundaries in optical astronomical images, where the background is dark and stars, the brightest objects, are uniform or increase in intensity toward their centers. For infrared ocean images, the aim is to find fronts and associated rings; for magnetograms, the aim is to find sunspots, magnetically active regions of the solar surface. Rings, fronts, and sunspots are not very star-like. Hence these images must be pre-processed before applying the method. The edges found from images processed with the algorithm are useful as inputs to other processing, e.g. in automated tracking of ocean features.

Velocity vectors for features of sequential oceanographic images

This paper investigates a fundamental problem of determining the position, orientation, and velocity field of the Gulf Stream in time-varying imagery. The authors propose an approximation method to characterize the deformation of these image motions for the purpose of estimating the velocity field of these images. The technique is focused on the interpretation of the change in the extracted features of the Gulf Stream. The underlying technique employs a triangulation of the region by a simplicial approximation of the velocity field on each triangle. A generalized computational framework, an outline of the mathematical foundation, and an implementation are presented.

Finding mesoscale ocean structures with mathematical morphology

Abstract We introduce a technique to aid in interpreting infrared satellite images of the North Atlantic Ocean Gulf Stream region. Present interpretive methods are largely manual, require significant effort, and are highly dependent on the interpreters skill. Our quasiautomated technique is based on mathematical morphology, specifically the image transformations of opening and closing, which are defined in terms of erosion and dilation. The implementation performs successive openings and closings at increasing thresholds until a stable division into objects and background is found. This method finds the North Wall of the Gulf Stream in approximately the same place as human analysts and another automated procedure, and does less smoothing of small irregularities than the other two methods. The North Wall is continuous and sharp except where obscured by clouds. Performance in locating warm-core eddies is also comparable to the other methods. However, the present procedure does not find cold-core rings well. We are presently investigating ways to reduce the effects of clouds and delete the unwanted water areas found by the method. We expect to be able to improve the cold-core eddy performance.

AN EXPERT SYSTEM FOR INTERPRETING MESOSCALE FEATURES IN OCEANOGRAPHIC SATELLITE IMAGES

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. This paper highlights the advantages of using the contextual information in the feature labeling algorithm. The need for an expert system and its feedback in automatic interpretation of oceanic features is discussed. The paper presents some important results of the series of experiments conducted at the Remote Sensing Branch, of the Naval Oceanographic and Atmospheric Research Laboratory, on the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (AVHRR) imagery data. The results clearly indicate the drastic improvement in labeling due to the oceanographic expert system.

Integrated oceanographic image understanding system

A system was assembled to study several aspects of locating ship targets from infrared imagery. The system was either placed on shore sites or installed on an aircraft to collect data on the scene. The primary sensor was an infrared camera which produced images of the scene at standard RS-l70 rates. Requirements that included real time operation dictated the use of a parallel architecture for this task. As no suitable commercial systems were avail able, a custom array of bit slice microprocessors was assembled for the task. Through extensive field tests strengths and limitations of the design have been identified. These lessons are being applied to the development of next generation systems. A gimbal mounted infrared camera with digitization circuitry presents a new 256 by 256 pixel image to the parallel pipelined array of 17 bit slice microprocessors thirty times a second. To extend processor performance beyond the standard commercial microprocessors. two basic bit slice designs were employed. The bit slice machines were highly tuned for the assigned tasks and algorithms. Unfortunately this restricted the desired flexibility to readily examine alternate algorithms. The fundamental architecture concept performed well quickly reducing the large array of data to manageable set of information. Real time operator displays were driven to monitor the progress of each test run. Results of the system operation were stored on video and digi tal recorders permitting more detailed analysis after each test. Non real time data reduction provided many insights into the system operation and to algorithm improvements. Substantial operator interaction. and data interpretation was required greatly slowing the post test analysis phase. Overwhelmed with data, the analysts focused on locating a few data segments of interest. Significant work remains in improving the interfaces between the field data and the powerful laboratory computers. Automation of the data analysis is also needed to efficiently evaluate the great volume of field information. Continuing improvements in Artificial Intelligence, Expert Systems, Neural Networks, and other areas may help here.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Topographic-based feature labeling for infrared oceanographic images

Abstract A general computational framework is proposed to address the problem of labeling oceanographic images. The essential ideas stem from fitting a bicubic polynomial to each pixels neighborhood and assigning topological labels based on the first and second directional derivatives of the polynomial surface. The topographic approach has been successfully applied by numerous previous investigators to other problems. The motivation of our work is to build a complete autonomous and self-contained module that segments and labels oceanographic images. In this paper, the relationship between the oceanographic features in infrared satellite imagery and the topographic structures is presented. Algorithms are developed that demonstrate the ability to locate and identify the North and South Walls of the Gulf Stream and to find approximate centers of warm and cold eddies. Experimental results on detecting these oceanographic features are provided.

An Expert System For Interpreting Mesoscale Features In Oceanographic Satellite Images

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.