Node-splitting optimized canonical correlation forest algorithm for sea fog detection using MODIS data.

Abstract

In this paper, a node splitting optimized canonical correlation forest algorithm for sea fog detection is proposed by using active and passive satellites. The traditional canonical correlation forest (CCF) algorithm insufficiently accounts for the spectral characteristics and the reliability of each classifier during integration. To deal with the problem, the information gain rate of node entropy is used as the splitting criterion, and the spectral characteristics of clouds and fogs are also combined into the model generation process. The proposed algorithm was verified using the meteorological station data and compared with five state-of-the-art algorithms, which demonstrated that the algorithm has the best performance in sea fog detection and can identify mist better.