<i>Ulva polifera</i> Detection from High Resolution Remote Sensing Images Based on Dual-Path Convolutional Neural Networks

WANG Yanli; DONG Zhipeng; WANG Mi

doi:10.13203/j.whugis20230159

Volume 49 Issue 12

Dec. 2024

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Geomatics and Information Science of Wuhan University > 2024 > 49(12): 2261-2270. > DOI: 10.13203/j.whugis20230159

WANG Yanli, DONG Zhipeng, WANG Mi. Ulva polifera Detection from High Resolution Remote Sensing Images Based on Dual-Path Convolutional Neural Networks[J]. Geomatics and Information Science of Wuhan University, 2024, 49(12): 2261-2270. DOI: 10.13203/j.whugis20230159

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Ulva polifera Detection from High Resolution Remote Sensing Images Based on Dual-Path Convolutional Neural Networks

1.
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao266590, China
2.
The First Institute of Oceanography, Ministry of Natural Resources, Qingdao266061, China
3.
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing,Wuhan University, Wuhan430079, China

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Received Date: May 03, 2023
Available Online: September 07, 2023

Graphical Abstract

Abstract

Abstract

Objectives
The green tide formed by Ulva prolifera (U. prolifera) is a harmful marine ecological 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 detection method for HSRIs based on dual-path convolutional neural networks (CNN) is proposed in this paper. First, a dual-path CNN semantic segmentation framework is designed based on the distribution characteristics of U. prolifera in HSRIs. The area and boundary of U. prolifera in HSRIs can be extracted simultaneously using the proposed framework. Then, the strategy for optimizing the initial U. prolifera area detection 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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References

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Ulva polifera Detection from High Resolution Remote Sensing Images Based on Dual-Path Convolutional Neural Networks

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