Abstract:

Aiming at the problems of low accuracy and poor real-time performance of Noctiluca scintillans red tide extraction in the field of satellite remote sensing， a Noctiluca scintillans red tide extraction method from Unmanned Aerial Vehicle （UAV） images based on deep learning was proposed. Firstly， the high-resolution RGB （Red-Green-Blue） videos collected by UAV were used as the monitoring data， the backbone network was modified to VGG-16 （Visual Geometry Group-16） and the spatial dropout strategy was introduced on the basis of the original UNet++ network to enhance the feature extraction ability and prevent the overfitting respectively. Then， the VGG-16 network pre-trained by using ImageNet dataset was applied to perform transfer learning to increase the network convergence speed. Finally， in order to evaluate the performance of the proposed method， experiments were conducted on the self-built red tide dataset Redtide-DB. The Overall Accuracy （OA）， F1 score， and Kappa of the Noctiluca scintillans red tide extraction of the proposed method are up to 94.63%， 0.955 2， 0.949 6 respectively， which are better than those of three traditional machine learning methods — K-Nearest Neighbors （KNN）， Support Vector Machine （SVM） and Random Forest （RF） as well as three typical semantic segmentation networks （PSPNet （Pyramid Scene Parsing Network）， SegNet and U-Net）. Meanwhile， the red tide images of different shooting equipment and shooting environments were used to test the generalization ability of the proposed method， and the corresponding OA， F1 score and Kappa are 97.41%， 0.965 9 and 0.938 2， respectively， proving that the proposed method has a certain generalization ability. Experimental results show that the proposed method can realize the automatic accurate Noctiluca scintillans red tide extraction in complex environments， and provides a reference for Noctiluca scintillans red tide monitoring and research work.

Key words: Noctiluca scintillans, red tide, Unmanned Aerial Vehicle (UAV), deep learning, UNet++, video processing

摘要：

针对目前卫星遥感中夜光藻赤潮识别精度低、实时性差的问题，提出一种基于深度学习的无人机（UAV）影像夜光藻赤潮提取方法。首先，以UAV采集的高分辨率夜光藻赤潮RGB视频影像作为监测数据，在原有UNet++网络基础上，通过修改主干模型为VGG-16，并引入空间dropout策略，分别增强了特征提取能力并防止过拟合；然后，使用ImageNet数据集预先训练的VGG-16网络进行迁移学习，以提高网络收敛速度；最后，为评估所提方法的性能，在自建的赤潮数据集Redtide-DB上进行实验。所提方法的夜光藻赤潮提取总体精度（OA）为94.63%，F1评分为0.955 2，Kappa为0.949 6，优于K近邻（KNN）、支持向量机（SVM）和随机森林（RF）这3种机器学习方法及3种典型语义分割网络（PSPNet、 SegNet和U-Net）。在模型泛化能力测试中，所提方法对不同拍摄设备和拍摄环境的夜光藻赤潮影像表现出一定泛化能力，OA为97.41%，F1评分为0.965 9，Kappa为0.938 2。实验结果表明，所提方法可以实现夜光藻赤潮自动化、高精度的提取，可为夜光藻赤潮监测和研究工作提供参考。

关键词: 夜光藻, 赤潮, 无人机, 深度学习, UNet++, 视频处理