基于图像帧间信息和FS-KFDA的极光序列图像检测算法

Aurora time-series image detection based on sample inter-frame correlation and FS-KFDA

为了解决数据量巨大的极光序列图像难于用人工划分的方法来进行变化监测的问题,提出了一种基于极光序列图像特征和帧间信息的感兴趣极光变化区域(ROI)自动检测算法,用计算机进行辅助分类。首先提取样本图像特征,再利用离散小波变化算法对帧间信息进行特征分析,引入特征标度核Fisher分析算法(FS-KD-FA),结合K-均值聚类选择训练样本,构建分类器实现了变化检测。对北极黄河站采集到的实测极光图像数据进行极光区域划分检测,实验结果表明了算法的有效性。

Using artificial testing efficiently is technically difficult because of the large amounts of data which must be processed.So,according to the feature and correlation of aurora time-series image,this paper proposed an algorithm based on image segmentation to extract region of interest（ROI） of change.The analysis started with a feature extraction of the input sequence from the spatial domain.Then,it considered correlation between images in a close time sequence,proposed discrete wavelet transform（DWT） to analyze the correlation for the sake of their representative.It proposed K-means clustering to select training samples,and used feature-scaling kernel Fisher discriminant analysis（FS-KFDA） which was a modified kernel Fisher discriminant analysis to train and build classifiers to extract ROI base on the training samples.Experiments carried out on the real aurora image database from Chinese Arctic Yellowriver station point out the effectiveness of the proposed algorithm,which results in an increase of segmentation precision with respect to conventional algorithms.