An information theoretic comparison of projection pursuit and principal component features for classification of Landsat TM imagery of central Colorado
An information theoretic comparison of projection pursuit and principal component features for classification of Landsat TM imagery of central Colorado
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DOI：
10.1080/01431160050121339
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发表时间：
2000-01
影响因子：
3.4
通讯作者：
C. Bachmann;T. Donato
中科院分区：
文献类型：
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作者：
C. Bachmann;T. Donato
比较了投影寻踪（PP）和主成分分析（PCA）方法对科罗拉多中部陆地卫星专题成像仪（TM）影像的预测结果。虽然PCA是PP算法的一般类的一个简单子集，但它不能区分高斯分布和非高斯分布，因为它最大化了投影方差。PP算法可最大化高阶统计量，可用于查找倾斜或多模态投影，以揭示底层类别结构。这些数据预测对基本的土地覆盖分布有更大的保真度。在隔离测试数据上，PP预测将单个类别的分离从几个百分比提高到24%。PP性能超过PCA的所有，但14个土地覆盖类别之一。
Projection pursuit (PP) and principal component analysis (PCA) projections derived from Landsat Thematic Mapper (TM) imagery of central Colorado were compared. While PCA is a simple subset of the general class of PP algorithms, it cannot distinguish Gaussian from non-Gaussian distributions, since it maximizes projected variance. PP algorithms, which maximize higher-order statistics, can be used to find skew or multi-modal projections in order to reveal underlying class structure. These data projections have greater fidelity to underlying land-cover distributions. On sequestered test data, PP projections improved separation of individual categories from a few percent to as much as 24%. PP performance exceeded that of PCA for all but one of the 14 land-cover categories.