Institute of Electrical and Electronics Engineers Inc.;New York, NY: IEEE
摘要:
摘要： This paper presents a novel approach for the feature extraction of hyperspectral image cubes. In this paper, hyperspectral image cubes are treated as volumetric data sets. Features that are most helpful in separating different targets are effectively extracted from the hyperspectral image cubes using a newly developed high-order texture analysis method. The traditional texture measure of the gray-level cooccurrence matrix is extended to a 3-D tensor field to explore the complicated volumetric data more effectively and to extract discriminant features for better classification. As the kernel size is one of the most important parameters in statistics-based texture analysis, a semivariance analysis and a spectral separability measure are used to determine the most appropriate kernel size in the spatial and spectral domains, respectively, for computing 3-D gray-level cooccurrence. In addition, a few statistical indexes are also extended to third-order forms in order to calculate quantitative texture properties of the generated cooccurrence tensor field. An airborne hyperspectral data set and an EO-1 Hyperion image are used to test the performance of the developed algorithms. Experimental results indicate that the developed 3-D texture analysis outperforms conventional second-order texture descriptors and the support vector machine-based classifier in supervised classifications of both hyperspectral data sets. 其他題名： TGRS 出版者： New York, NY: IEEE 出版日期： 2013-06-01 出處： IEEE Transactions on Geoscience and Remote Sensing, 2013-06, Vol.51 (6), p.3504-3513 資源來源： IEEE Electronic Library (IEL) 版權： 2014 INIST-CNRS 識別號： ISSN: 0196-2892 識別號： EISSN: 1558-0644 識別號： DOI: 10.1109/TGRS.2012.2223704 識別號： CODEN: IGRSD2
