A Study On Polarimetric Feature Exctraction And Classification Of Marine Algae Based On Deep Learning

As a damage-free,label-free,non-contact and easily expandable optical technology,polarimetric technology is capable of capturing anisotropic structural information of objects,therefore it has unique application adavantages in observing marine algae.The Mueller matrix,which is an important descriptor and research target in the field of polarization,contains all the polarimetric information of the mearsured object.The Mueller matrix microscopic imaging technology reveals great application potentials in studying the anisotropic characteristics of micro-sclae marine algae.Based on the Mueller matrix microscopic imaging technology,this thesis implements the measurement and calibration of the marine algal Mueller matrices by the double wave-plate rotating method and the correspondding air calibration.More than 10,000 marine algal samples have been mearsured by the Mueller matrix microscope,and the corresponding sample processing scheme and data processing pipeline are established.The polarimetric effects of various classes of marine algal samples are analyzed.The measured marine algal samples,which have study significations in marine ecological researches,are mainly collected in the surrounding waters of Shenzhen,including several classes of red tide algae and non-red tide algae that vary morphologically in microscale.With the help of various algorithms such as deep learning,this thesis focuses on the feature extraction and classification of marine algal Mueller matrix,and fully considers the technical difficulties of high-dimensional polarimetric data processing and the characteristics of marine algal Mueller matrices.A variety of deep convolutional neural network architectures are designed with low model complexity,and practical classification results are obtained on the classification accuracies and F1 scores.The effectiveness of polarimetric technology in the classification of marine algae has been demonstrated.In the problem of polarimetric feature extraction of marine algal Mueller matrix,this thesis starts from the view point of statistics,and quantitatively describes the statistical distribution information of marine algal Mueller matrix with statistical moments as a preliminary assessment of the difficulty of the problem,then compares the technical path and the algorithmic results of polarimetric feature extraction of multiple metric learning algorithms.The siamese network algorithm is introduced,and the algorithmic results are compared quantitatively with the evaluation methods and indicators in the framework of classification problems.By repeatedly testing the polarimetric data of marine algal samples and various siamese network architectures,the siamese network algorithm proves to be suitable for the problem of polarimetric feature extraction of marine algae.The Pearson correlation coefficient is introduced to analyze the statistical correlation between the features in the first layer of the siamese network and the Mueller matrix elements,and the relevant experimental phenomena and technical experience are summarized.In this thesis,the Muller matrix imaging technology and the deep learning algorithm are combined effectively.Based on the deep learning algorithms such as the convolutional neural networks and the siamese networks,a big data method that is suitable for the polarimetric feature extraction and classification of marine algae has been established.With the information provided by polarimetric technology,this big data approach may help study the researches in marine ecology including complex anisotropic biological systems.