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.
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