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The detection and removal of erroneous pixels is a critical pre-processing step in producing chlorophyll-a (chl-a) concentration values to adequately understand the bio-physical oceanic process using optical satellite data. Geostationary Ocean Color Imager (GOCI) chl-a images revealed that numerous speckle noises with enormously high and low values were randomly scattered throughout the seas around the Korean Peninsula as well as in the Northwest Pacific. Most of the previous methods used to remove abnormal chl-a concentrations have focused on inhomogeneity in spatial features, which still frequently produce problematic values. Herein, a scheme was developed to detect and eliminate chl-a speckles as well as erroneous pixels near the boundary of clouds; for the purpose, a deep neural network (DNN) algorithm was applied to a large-sized GOCI database from the 6-year period of 2012–2017. The input data of the proposed DNN model were composed of the GOCI level-2 remote-sensing reflectance of each band, chl-a concentration image, median filtered, and monthly climatology chl-a image. The quality of the individual images as well as the monthly composites of chl-a data was improved remarkably after the DNN speckle-removal procedure. The quantitative analyses showed that the DNN algorithm achieved high classification accuracy with regard to the detection of error pixels with both very high and very low chl-a values, and better performance compared to the general arithmetic algorithms of the median filter and threshold scheme. This implies that the implemented method can be useful for investigating not only the short-term variations based on hourly chl-a data but also long-term variabilities with composite products of the GOCI chl-a concentration over the span of a decade.

Application of Deep Learning for Speckle Removal in GOCI Chlorophyll-a Concentration Images (2012–2017)