基于深度学习的GF-1卫星WFV影像赤潮探测方法

Red tide detection using GF-1 WFV image based on deep learning method

赤潮是我国主要的海洋生态灾害,有效监测赤潮的发生和空间分布对于赤潮的防治具有重要意义。传统的赤潮监测以低空间分辨率的水色卫星为主,但是其对于频发的小规模赤潮存在监控盲区。GF-1卫星WFV影像具有空间分辨率高、成像幅宽大和重访周期短等优点,在小规模赤潮监测中表现出较大的潜力。然而,GF-1卫星WFV影像的光谱分辨率较低,波段少,传统面向水色卫星的赤潮探测方法无法应用于GF-1卫星WFV数据。而且赤潮具有形态多变、尺度不一的特点,难以精确提取。基于此,本文提出了一种面向GF-1卫星WFV影像的尺度自适应赤潮探测网络(SARTNet)。该网络采用双层主干结构以融合赤潮水体的形状特征与细节特征,并引入注意力机制挖掘不同尺度赤潮特征之间的相关性,提高网络对复杂分布赤潮的探测能力。实验结果表明,SARTNet赤潮探测精度优于现有方法,F1分数达到0.89以上,对不同尺度的赤潮漏提和误提较少,且受环境因素的影响较小。

Red tide is a major marine ecological disaster in China.Effectively monitoring the occurrence and spatial distribution of red tide is of great significance for their prevention and control.Traditional red tide monitoring is mainly conducted by watercolor satellites with low spatial resolution.However,there are monitoring blind areas for frequent small-scale red tides.GF-1 WFV remote sensing images,featuring high spatial resolution and a wide imaging range,can be used to monitor small-scale red tides.However,the traditional method for watercolor satellites cannot be used for GF-1 WFV satellite data as GF-1 WFV remote sensing images are characterized by low spectral resolution and few bands.And it is hard to extract the information about red tide as they differ in both shape and scale.Due to diverse shapes of the red tide distribution,this paper proposes a scale-adaptive red tide detection network(SARTNet)for GF-1 WFV sensing images.This network adopts a two-layer backbone structure to integrate the shape and detail features of red tide and introduces an attention mechanism to model the correlation between features of red tides at different scales,thereby improving its performance in detecting red tides that are complexly distributed.The experimental results show that the red tide detection performance of SARTNet is better than that of the existing methods,with an F1 score above 0.89;and it is less affected by environmental factors,with few missing and misstated pixels for red tide information at different scales.