期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII Part B7
页码：443-446
出版社：Copernicus Publications
摘要：Information extraction from Hyperspectral imagery is highly affected by difficulties in accounting for flux density variation and Bidirectional reflectance effects. Calculation of flux density requires digital description of the surface structure at the pixel level, which is frequently not available at the accuracy required (if exists). The result of these shortcomings in achieving accurate radio- metric image calibration is reduced separability of surface types: limiting the performance of spectral classification schemes. In this study an alternative approach is presented: application of features of the spectral signature which mainly represent the shape of the spectral curve. This is achieved by applying features calculated based on Wavelet deco
关键词：Remote Sensing; Hyperspectral; Classification; Calibration; Analysis; Data mining
