Application of Semi-Supervised Dimensionality Reduction for Hyperspectral Image Classification

摘要：

Dimensionality reduction techniques have become an important issue concerns of hyper-spectral image processing and application A semi-supervised dimensionality reduction (SSDR) for classification of hyper-spectral image is applied in this paper. This method employed both labeled and unlabeled data with pairwise-constraints to obtain a set of projective vectors such that intrinsic structures of image as well as the pairwise constraints can be preserved in the projective low-dimensional space. To evaluate the method, a case study of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) image is implemented, and the experimental results validate the applicability and effective of the algorithm. Comparisons with principal component analysis (PCA) and Fisher discriminate analysis (FDA) are also conducted, and the result demonstrates that the SSDR can significantly improve classification accuracy.

关键词： semi-supervised dimensionality reduction hyperspectral image classification

作者: Cao Senmao Wu Bo

作者单位: Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou Univers Spatial Information Research Center of Fujian Province, Fuzhou University Fuzhou 350002, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 14-17

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）