GOCI super-resolution reconstruction based on transfer learning and detection of marine floating algae
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.
NETL
NSTL
万方数据
