Ulva polifera Detection from High Resolution Remote Sensing Images Based on Dual-Path Convolutional Neural Networks
Objectives:The green tide formed by Ulva prolifera(U.prolifera)is a harmful marine ecologi-cal disaster.The rapid and accurate detection is of great significance for timely management of U.prolifera and the healthy development of the marine industry.Methods:Because the boundary of U.prolifera area is difficult to be determined accurately in high resolution remote sensing images(HSRIs),an U.prolifera de-tection method for HSRIs based on dual-path convolutional neural networks(CNN)is proposed in this pa-per.First,a dual-path CNN semantic segmentation framework is designed based on the distribution charac-teristics of U.prolifera in HSRIs.The area and boundary of U.prolifera in HSRIs can be extracted simulta-neously using the proposed framework.Then,the strategy for optimizing the initial U.prolifera area de-tection results based on U.prolifera boundary is proposed to improve the detection accuracy.Results:The experimental results show that the proposed method can extract U.prolifera accurately,with F1-score of 88.25％,intersection-over-union of 78.97％and over accuracy of 98.99％,which is better than other U.prolifera detection algorithms.Conclusions:The proposed method can obtain good results for the detection of different types of U.prolifera in HSRIs.
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