Multi-Image Edge Detection for SST Images

Jean-François Cayula Graduate School of Oceanography, University of Rhode Island, Narragansett, Rhode Island Planning Systems Incorporated, Stennis Space Center, Mississippi

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Peter Cornillon Graduate School of Oceanography, University of Rhode Island, Narragansett, Rhode Island

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Abstract

This paper presents an approach based on the analysis of an image sequence to detect temperature fronts in a sea surface temperature image. The multi-image edge detection algorithm starts by applying a single-image edge detection algorithm to the sequence of images under study. Next, fronts or portions of fronts, which were detected in neighboring images by the single-image algorithm and which match features in the current image, are identified as persistent. The coordinates of these persistent fronts are then passed to the single-image edge detection algorithm so that additional fronts can be detected. The performance of the multi-image edge detection algorithm, of various single-image algorithms, and of a human expert are evaluated on a set of 98 images. For that purpose, the location of the fronts obtained by applying various methods to the SST images is compared to the in situ measures of the Gulf Stream position. With respect to both quality and the number of detected edges, the multi-image edge detection algorithm is the only automated method that achieves results comparable to those obtained by a human expert.

Abstract

This paper presents an approach based on the analysis of an image sequence to detect temperature fronts in a sea surface temperature image. The multi-image edge detection algorithm starts by applying a single-image edge detection algorithm to the sequence of images under study. Next, fronts or portions of fronts, which were detected in neighboring images by the single-image algorithm and which match features in the current image, are identified as persistent. The coordinates of these persistent fronts are then passed to the single-image edge detection algorithm so that additional fronts can be detected. The performance of the multi-image edge detection algorithm, of various single-image algorithms, and of a human expert are evaluated on a set of 98 images. For that purpose, the location of the fronts obtained by applying various methods to the SST images is compared to the in situ measures of the Gulf Stream position. With respect to both quality and the number of detected edges, the multi-image edge detection algorithm is the only automated method that achieves results comparable to those obtained by a human expert.

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