基于迁移学习的GOCI超分辨率重建与海洋漂浮藻类探测  

GOCI super-resolution reconstruction based on transfer learning and detection of marine floating algae

作　　者：朱红春[1] 朱国灿 李金宇 张怡宁 芦智伟 杨延瑞 刘海英 ZHU Hongchun;ZHU Guocan;LI Jinyu;ZHANG Yining;LU Zhiwei;YANG Yanrui;LIU Haiying(College of Geodesy and Geomatics,Shandong University of Science and Technology,Qingdao 266590,China;Shandong Consultant Association of Ocean Engineering,Jinan 250013,China;College of Computer Science and Engineering,Shandong University of Science and Technology,Qingdao 266590,China)

机构地区：[1]山东科技大学测绘与空间信息学院,山东青岛266590 [2]山东省海洋工程咨询协会,山东济南250013 [3]山东科技大学计算机科学与工程学院,山东青岛266590

出　　处：《山东科技大学学报（自然科学版）》2024年第2期40-48,共9页Journal of Shandong University of Science and Technology(Natural Science)

基　　金：国家自然科学基金项目(41971339);山东科技大学科研创新团队支持计划项目(2019TDJH103)。

摘　　要：遥感技术是进行海洋漂浮藻类目标识别与变化监测的重要手段。GOCI遥感卫星影像具有高时间分辨率、低空间分辨率的特点,其低空间分辨率影响了海洋漂浮藻类遥感探测的效果。本研究通过对具有较高空间分辨率的Sentinel-2遥感卫星影像结构特征的迁移学习,应用ESRGAN超分辨率重建技术,将GOCI影像的空间分辨率提升至125 m;在此基础上,构建了基于超分辨率重建GOCI遥感影像的U-Net深度学习语义分割网络,实现了海洋漂浮藻类的较高精度探测。实验结果表明:超分辨率重建的GOCI影像显著提升了影像的空间细节清晰度,基于此实现的海洋漂浮藻类探测结果取得了较高的精度,其中面积相对误差下降了51.87%,F1值提高了2.41%。本研究是应用GOCI遥感影像进行海洋漂浮藻类高精度探测的一次成功实践,为实现海洋目标的动态精细化监测提供有益的参考。Remote sensing technology is an important means for detecting and monitoring changes in floating algae in the ocean.GOCI remote sensing satellite images have the characteristics of high-temporal and low-spatial resolution.Its low spatial resolution affects the effect of remote sensing detection of marine floating algae.In this paper,through the transfer learning of the structural characteristics of Sentinel-2 remote sensing satellite images with high-spatial resolution,the spatial resolution of the GOCI images were enhanced to 125 m by using ESRGAN super-resolution reconstruction technology.On this basis,a U-Net deep learning image segmentation network based on super-resolution reconstructed GOCI remote sensing images was constructed.This network was used to achieve higher-precision detection of marine floating algae.The experimental results showed that the super-resolution reconstructed GOCI images significantly improved the spatial detail clarity of images and the detection results of marine floating algae achieved high accuracy,with a reduction of 51.87%in the area relative error and an increase of 2.41%in the F 1 value.As a successful practice in enhancing the accuracy of detecting marine floating algae targets using GOCI remote sensing images,this study provides a valuable reference for achieving the dynamic and fine-grained monitoring of marine targets.

关 键 词：GOCI影像 数据融合 超分辨率重建 海洋漂浮藻探测 深度学习 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置] X55[自动化与计算机技术—控制科学与工程]