Automatic Classification of Dayside Aurora in All-Sky Images Using A Multi-Level Texture Feature Representation
In this paper, we propose an aurora classification method using a multi-level feature representation aimed to capture both global and local texture information, and to reduce the feature space dimension substantially.First-order and second-order statistics are computed for an input image and its low-frequency scaled images at three lower levels obtained using wavelet decomposition.The features include gray level distribution, co-occurrence matrix features, and run-length matrix features.A support vector machine (SVM) classifier was trained and tested on a Chinese Arctic Yellow River Station dayside aurora image dataset.Classification performance was evaluated and compared with those of k-nearest neighbor (KNN) classifiers and backpropagation neural networks (BPNN).To explore the possibility of using a smaller feature space, we used a Minimum-Redundancy Max-Relevance feature selection strategy.The result shows that there is only indistinct performance decrease by reducing the feature vector from a total of 88 to the most discriminatory 38 features.This proves that our multi-level feature representation is very robust.
aurora wavelet decomposition gray level co-occurrence matrix run-length support vector machine
Shenmiao Han Zhensen Wu Guangli Wu Jun Tan
School of Science,Xidian University,Xian,710071,China Department of Radiology,University of Pittsburgh,Pittsburgh,PA,15213,USA
国际会议
厦门
英文
158-162
2011-07-08（万方平台首次上网日期，不代表论文的发表时间）