基于多尺度特征融合的恒星光谱分类方法

Classification of Star Spectrum Based on Multi-Scale Feature Fusion

近年来,随着各大光谱巡天项目的陆续实施,观测得到的天体光谱数据急剧增长。大型光谱巡天项目对光谱的自动分类和分析提出了更高的要求。采用多尺度特征融合模块来获取光谱在不同尺度上的光谱特征,结合CNN网络在分类任务上的优势,提出了一种基于多尺度特征融合的恒星光谱分类模型(MSFnet),对恒星光谱进行光谱型预测。主要包含多尺度特征融合模块和一个含4个卷积层,2个最大池化层,1个全连接层的卷积神经网络。为了防止出现过拟合的问题,添加了dropout,添加dropout后可以使得模型不依赖某些局部特征,防止过拟合,优化网络的鲁棒性。实验中的数据集均来自LAMOST DR9数据库,在输入到模型进行训练之前,需要对光谱数据进行预处理:重新对光谱进行均匀采样,之后进行最大最小值归一化。该实验的编程语言为python 3.9,引入了Pytorch深度学习框架来构建网络。实验部分为两部分:第一部分是研究卷积神经网络的层数、特征图个数与准确率的关系;第二部分将本文提出的MSFnet模型和Resnet18模型的结果对比实验,从精准率P、召回率R、调和平均值F1、准确率A、运行时间等指标来对两个模型进行对比评估。两个模型所采用的训练集、验证集和测试集均按6∶2∶2的比例进行分配,保证了两个模型的训练样本一致。结果表明,采用4个卷积层、特征图数量为16的卷积神经网络的准确率最高。基于此结论,本文提出了特征融合模块与卷积神经网络的组合MSFnet模型,相对于18层的残差神经网络模型,该模型的结构更简单,在上述指标的表现上也与Resnet18模型相当,并且在A、F、K型光谱的分类效果更好,速度更快。MSFnet模型在测试集上的准确率接近97%,比传统的CNN和Resnet18模型的准确率更高,表明了MSFnet模型有助于提升光谱自动分类的准确性。

In recent years,there has been a significant increase in the number of observed astronomical spectra.This has led to greater demands for automatic classification and analysis of spectra in large-scale spectroscopic surveys.In this study,we introduce a multi-scale feature fusion-based stellar spectral classification model(MSFnet)that leverages the strengths of Convolutional Neural Networks(CNNs)in classification tasks and employs a multi-scale feature fusion module to extract spectral features at various scales for the prediction of stellar spectral types.The proposed MSFnet architecture consists primarily of a multi-scale feature fusion module and a CNN with four convolutional layers,two max-pooling layers,and one fully connected layer.To mitigate overfitting,dropout is incorporated into the model,enhancing its robustness by reducing dependence on specific local features.The dataset employed in this study is derived from the LAMOST DR9 database.Before training,data preprocessing is performed,which includes uniform resampling of spectra and min-max normalization.The experiment uses Python 3.9 and the PyTorch deep learning framework to build the network.The experimental section is divided into two parts:the first part investigates the relationship between the number of layers in the CNN,the number of feature maps,and the classification accuracy;the second part compares the performance of the proposed MSFnet model and the Resnet18 model using evaluation metrics such as precision(P),recall(R),and F1 score.Both models'training,validation,and test sets are split according to a 6∶2∶2 ratio to maintain consistency in training samples.Results demonstrate that the highest accuracy is achieved using a CNN with four convolutional layers and 16 feature maps.Based on this finding,we propose the MSFnet model,which combines the feature fusion module with the CNN.Compared to the 18-layer residual neural network model,the MSFnet model has a more straight forward structure and performs similarly on the evaluation metrics.The performance in the metrics above is on par with that of the Resnet18 model.Furthermore,it demonstrates superior classification efficacy for spectra types A,F,and K,accompanied by enhanced speed.With an accuracy of nearly 97%on the test set,the MSFnet model outperforms traditional CNN and Resnet18 models,indicating its potential to improve the accuracy of automatic spectral classification significantly.