Ocean Front Detection From Instant Remote Sensing SST Images

Abstract

Identifying fronts manually from satellite images is a tedious and subjective task. Accordingly, edge detection algorithms are introduced for automatic detection of fronts. However, traditional algorithms cannot be applied to cloud-contaminated images, because missing data caused by occasional cloud coverage interferes with front detection. To diminish this risk, this letter proposes a new algorithm for a quick and an accurate detection of fronts from an instant cloud-contaminated sea surface temperature (SST) image, instead of depending on the daily or weekly averaged SST images. This algorithm adopts a data-driven analog interpolation method, which estimates missing values from the historical data of the same region. After reducing the contour between the interpolated data and the original data, an instant front detection algorithm is proposed based on microcanonical multiscale formalism (MMF). The algorithm utilizes MMF to detect singularity exponents (SEs), and then enhances the features detected in a cloud-contaminated region. Finally, a threshold is set to extract fronts from SE. Experimental results on an AVHRR satellite SST image of 12:00 o’clock covering China Coastal waters confirmed the effectiveness of the proposed algorithm.