基于AlexNet的近岸水生植物光谱分类及特征光谱分析

Spectral Classification and Characteristic Spectral Analysis of Nearshore Aquatic Plants Based on AlexNet

水生植物能够净化污染物和抑制藻类生长,在生态系统重建方面具有重要的应用价值。光谱分析作为植物种类识别的一种方法,具有无接触、快速、无污染等特点。受周围水环境的影响,绿色水生植物的光谱特征峰比陆生植物更加难以区分,地面实测光谱数据不仅维度高,且存在大量重叠谱带和背景干扰,特征光谱不明显;同时,通过地面实测获取样本数据较为困难,适用于深度学习的地面光谱数据集较少。针对以上问题,本文提出了一种基于一阶导数法结合AlexNet网络的分类模型。本团队以2019年9—10月上海河道内4种优势种群的近岸挺水水生植物为研究对象,使用地物光谱仪采集4种水生植物叶片部位的光谱信息。实验中,首先使用4种光谱分析法对原始数据进行预处理,比较预处理前后分类模型的准确率,其中一阶导数法结合AlexNet网络的分类模型对4种水生植物的分类精度最高,为99.50%;然后分别选取样本数据的40%、60%和80%作为训练集,验证模型在小样本下的泛化能力;最后利用Grad-CAM算法对模型进行可视化,分析后发现本文模型提取的水生植物的特征光谱与现有研究结果一致。上述研究结果表明,本文模型能够有效提取水生植物的特征光谱,实现对4种水生植物的快速准确分类识别,为高光谱遥感卫星识别此4种水生植物提供了重要参考。

Objective Aquatic plants can purify pollutants and inhibit algae growth.Therefore,obtaining accurate information on the number and growth status of aquatic plant species helps monitor the aquatic ecological environment.Spectral analysis,as a vital method for aquatic plant identification,has the characteristics of noncontact,fast,and pollution-free.However,because they are affected by the surrounding water environment,the characteristic spectral peaks of green aquatic plants are more challenging to distinguish than terrestrial plants.The ground spectral data have high dimensions and numerous overlapping bands and background interferences,and the characteristic spectrum is not obvious.The data are more challenging,and a few ground spectral datasets are suitable for deep learning.Currently,conventional machine learning classification methods cannot accurately and comprehensively extract deep features on small samples,resulting in unsatisfactory final classification results.Therefore,the deep learning algorithm and hyperspectral data are used to classify aquatic plants for the problems of many overlapping spectral bands,background interference,inconspicuous characteristic peaks,and less self-built aquatic plant spectral sample data.Methods This study uses the first-order derivative method combined with the AlexNet network to classify and identify four nearshore aquatic plants.The classification accuracy and training speed of three convolutional neural networks(AlexNet,CNN3,and VGG16) were compared to verify the classification effect of our model on the nearshore aquatic plant spectrum and the AlexNet network was determined as the optimal network structure.Furthermore,the influence of the number of samples on different classification models was studied,and classification effect of three models under small samples was explored.The influence of spectral preprocessing on the model s classification effect was studied,and the sample data before and after preprocessing using four spectral preprocessing methods were compared.Finally,the Grad-CAM algorithm was used to study the classification model visually to extract the characteristic bands of four aquatic plants.The sensitive spectrum bands of nearshore aquatic plants were analyzed,extracted,and compared with the existing aquatic plant datasets.The results are compared to verify the effectiveness of the feature extraction of this study s model.Results and Discussions The classification model based on the first-order derivative combined with the AlexNet network can realize the fast and accurate classification and identification of this study s four aquatic plants.Compared with the VGG16 and CNN3networks,this studys model has the highest test accuracy of 99.50%.The models training and testing speeds are 13.56 s/epoch and0.032 frame/s,respectively,which are 30.12 s/epoch and 0.016 frame/s lower than those of VGG16.Although the model s training speed is 8 s/epoch higher than that of CNN3 and the testing speed is 0.002 frame/s higher,the classification accuracy is 14.44percentage points higher than that of the CNN3 model.To verify the model s classification accuracy under small samples,40%,60%,and 80%of the sample dataset were randomly selected as the training set.The lowest classification accuracy of the model was99.15%,higher than the classification accuracy of the CNN3 and VGG16 models.The influences of spectral overlapping bands and background interference on the classification results were reduced using four spectral preprocessing methods to process the sample data,and the classification accuracy of the three models before and after preprocessing was compared.The first-order derivative method improved the classification accuracy.The first-order derivative combined with the AlexNet network has the highest classification accuracy of 99.50%.The Grad-CAM algorithm was used to visualize the established aquatic plant identification model,and the classification-sensitive bands of four aquatic plants were analyzed,including seven classification sensitive bands for Typha angustifolia L.,two classification sensitive bands for Pontederia cordata L.,eight classification sensitive bands for Hydrocotyle vulgaris,and five classification sensitive bands for Thalia dealbata.Conclusions This study proposed a spectral classification method of nearshore aquatic plants based on the first-order derivative combined with the AlexNet network.Taking the four primary nearshore aquatic plants in typical river channels in Shanghai as the research objects,a deep learning model capable of accurately identifying nearshore aquatic plants was established.The results showed that the characteristic spectrum extracted using this study s model correlates with the characteristic spectral bands of similar nearshore aquatic plants extracted in other studies,indicating that the model is correct and effective for classifying four nearshore aquatic plants.By comparing the classification effects before and after spectral preprocessing,it is found that the first-order derivative spectral preprocessing method can effectively remove overlapping spectral bands and background interference,increase the model s convergence speed,and improve the model classification effect.The classification method combined with the first-order derivative method and the AlexNet network is applied to rapidly classify the spectrum of four aquatic plants:Typha angustifolia L.,Pontederia cordata L.,Hydrocotyle vulgaris,and Thalia dealbata.It provides an essential reference for classifying and identifying these four aquatic plants under hyperspectral remote sensing.