Applying Differentiable Mutual Information to Hyperspectral Band Selection

摘要：

In this paper, we extend our earlier work by improving a mutual information (MI) based hyperspectral band selection method. Mutual information effectively measures the statistical dependence between two random variables. By modeling ground truth (e.g., a reference map) as one of the two random variables, MI can be used to find the spectral bands that contribute most to image classification. We apply a differentiable rather a histogram-based representation of mutual information to construct the estimated reference map, which results in an automatic solution by gradient searching. Experiments on the AVIRIS 92AV3C data set show that the proposed approach can find the best spectral window, and the bands in this window can be used to construct the reference map satisfactorily.

作者: Baofeng Guo Yuesong Lin Dongliang Peng Anke Xue

作者单位: Institute of Information and Control School of Automation, Hangzhou Dianzi University Hangzhou, Zhejiang, 310018, China

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 1638-1642

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）