Sarah H. Peckinpaugh

Edge detection applied to satellite imagery of the oceans

A computer edge-detection algorithm for automatic delineation of mesoscale structure in digital satellite IR (infrared) images of the ocean is developed. The popular derivative-based edge operators are shown to be too sensitive to edge fine-structure and to weak gradients to be useful in this application. The edge-detection algorithm is based on the gray level cooccurrence matrix (GLC), which is commonly used in image texture analysis. The cluster shade texture measure derived from the GLC matrix is found to be an excellent edge detector that exhibits the characteristic of fine-structure rejection while retaining edge sharpness. This characteristic is highly desirable for analyzing oceanographic satellite images. >

An improved method for computing gray-level cooccurrence matrix based texture measures☆

Abstract This paper describes a machine efficient approach for computing texture measures based on the gray-level cooccurrence matrix. Several measures are discussed relative to the method. The method applied to the cluster shade texture measure is described completely. Several algorithm variations for computing the Cluster Shade texture measure are described and a comparison is made.

Circle detection for extracting eddy size and position from satellite imagery of the ocean

Evaluates the ability of several circle detectors to define the size and position of warm and cold eddies in oceanographic satellite imagery. The Advanced Very High Resolution Radiometer channel 4 (10.3-11.3 /spl mu/m) or sea surface temperature test images are reduced to binary edge images. Six different circle detectors are then applied to the edge images. The automated results are compared to eddies defined by a trained analyst. >

Comparative study of two recent edge-detection algorithms designed to process sea-surface temperature fields

Two algorithms used for the detection of fronts in satellite-derived sea-surface temperature fields are compared. The two algorithms produced surprisingly comparable results considering the substantial differences in the two approaches: multilevel (Algorithm 1) versus locally based (Algorithm 2). Algorithm 1 offers the advantage of shorter run times. Algorithm 2 can be made faster if one is willing to accept less reliable edge detection. Algorithm 1 also offers the advantage of being adaptive and therefore automatic in its application to different data sets. However, when direct control with regard to detection of the edges is demanded, Algorithm 2 contains two tunable parameters to select the smoothness and the strength of edges, while Algorithm 1 as presently coded does not. >

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.

Water Depth Determination Using Mathematical Morphology

Rapid determination of water depth near coastal areas is a practical problem of interest to Navy oceanographers and ships. Variations in depth both perpendicular and parallel to the shore are sought. Our aim is to create a semi-automated system for processing time sequences of remotely sensed images of wave crests to determine water depth. Using mathematical morphology to clean the images and find portions of contours parallel to the shoreline and time-stack images to determine wave phase speed both simplifies the analysis and requires significantly less processing time than previous manual or semi-automated methods. Depth results for the test image sequence discussed compare reasonably well to depths determined by sounding or ground sensors, but have large errors.