【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。更多还原

【Abstract】 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 F₁ 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.更多还原