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

Abstract

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.

Inter-Comparison of Methods for Chlorophyll-a Retrieval: Sentinel-2 Time-Series Analysis in Italian Lakes

“…With the recent availability of imagery captured by Landsat-8 Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI), there is a growing trend in applications of this imagery in mapping constituents of inland bodies of water [30,31]. This is mainly due to the enhanced radiometric resolution (12-bit) and high spatial resolution (10−30 m) of the imagery provided by these sensors, because the spatial resolution of ocean color sensors that are either sun-synchronous or geostationary are too coarse (hundreds of meters at the best) for most of the inland waters [32][33][34]. The first attempts to derive lake constituents using Sentinel-2 imagery have been made in Estonian lakes based upon empirical methods [31] and in an oligotrophic German lake using a physics-based method [35], which demonstrated promising potential.…”

A Systematic Review of the Application of GOCI to Water Quality Monitoring of Inland and Coastal Waters

“…The concentration of Chla, which is the most prevalent pigment in algae and relates to phytoplankton biomass [97,[122][123][124][125][126][127].Chla concentration is a key indicator of water quality and aquatic ecological health [128][129][130].…”

Research on concentration detection algorithm of embedded gas detection system based on deep learning

“…x y l x (10) By knowing the number of data points, calculate the coefficients of n linear scaling functions in the formula. In this method, when there are many segments, the interpolation error is smaller, but the complexity of the program will increase accordingly.…”

“…Exhaled breath is often closely associated with some metabolic diseases. For example, NO can be used as a chemical marker to detect asthma or airway inflammation [10]. The exhaled breath of lung cancer patients has much higher levels of cyclododecanetriene and benzoic acid than normal people [11].…”