engleski

Application of Cogent Confabulation Classifier to Bathing Water Quality Assessment Using Remote Sensing Data

Cogent Confabulation is a comprehensive and simple method for data classification, but is unjustly neglected in modern machine learning. Cogent confabulation uses multiple evidence to classify data items, requiring less computation than Bayesian classifiers. Earth observation with remote sensing provides researchers evidence of various events and processes encoded in the reflectance of the surface in multiple wavelength bands. Decoding which bands provide information on which event is in the focus of many scientists. In this paper we are presenting the preliminary results on using the Cogent Confabulation Classifier on Sentinel-3 OLCI satellite data to predict the status of bathing water quality. Measuring bathing water quality is an important activity to protect human health, animal health and the environment. It is based on in situ measuring bacteria and categorizing quality based on the EU Directive 2006/7/EC. Study area used in this paper is Kaštela Bay and Braˇc Channel located in the Republic of Croatia. Data set is constructed of satellite data (bands values) and ground truth water quality based on in situ measurements. We developed a classifier that was trained on the constructed data set and is able to classify two distinct classes of bathing water quality based on satellite images. Results of the applied classifier are described, analyzed, and compared with other commonly used classification approaches in terms of accuracy and computational performance.

Cogent Confabulation ; Sentinel-3 ; OLCI ; bathing water quality

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