基于自适应提升小波变换和LBP的极光分类算法

Aurora Classification Algorithm Based on Adaptive Lifting Wavelet Transform and LBP

摘要: 本文提出了一种新的基于自适应提升小波变换的双尺度算法、改进的局部二值模式和模糊近邻分类相结合的极光分类算法。该算法在极光图像预处理的基础之上，先是利用自适应提升的小波变换将原始的极光图像分为几个子图像，然后再对各个子图像进行变尺度的高斯滤波。用局部二值模式进行对子图像进行特征的提取，最后用模糊的近邻分类算法对其进行分类。仿真实验证明，首先本文算法的分类效率高于其他极光分类算法，其次就是本文算法对普通的噪声，例如高斯噪声和椒盐噪声，都有较好的鲁棒性。

Abstract: This paper presents a new dual-scaling algorithm based on adaptive lifting wavelet transform and improved Local Binary Pattern and classification of a combination of fuzzy neighbor Aurora classi-fication algorithm. Based on the aurora image preprocessing, the algorithm is first using adaptive lifting wavelet transform of the original image to divide into several sub-images of Aurora, and then for each sub-image variable scale Gaussian filter, and to conduct sub-picture with the local binary pattern feature extraction, and finally with fuzzy neighbor classification algorithm to classify. Simulation results show that, first, the algorithm classification efficiency is higher than other Aurora classification algorithm, followed by the algorithm for ordinary noise, such as Gaussian noise and salt and pepper noise having better robustness.