Detection and Analysis of Marine Green Algae Based on Artificial Intelligence

Abstract

This chapter developed a deep-learning (DL) model for floating U. prolifera detection in the Yellow Sea based on the U-Net framework with overfitting prevention. Based on the 1,055/4,071 pairs of labelled samples, the model reached an accuracy of 97.51 (99.83)% and an Intersection over Union (IoU) of 42.62 (88.09)% for the MODIS (SAR) images. We processed satellite images containing U. prolifera using the DL model and drew an exciting finding: since SAR (MODIS) detect the floating (and submerged) parts of U. prolifera respectively, we defined a floating and submerged ratio number (FS ratio) to be a good indicator for representing different life phases of U. prolifera algae.