On the impact of PCA dimension reduction for hyperspectral detection of difficult targets

被引:228
作者
Farrell, MD [1 ]
Mersereau, RM [1 ]
机构
[1] Georgia Inst Technol, Ctr Signal & Image Proc, Atlanta, GA 30332 USA
关键词
adaptive detection; dimension reduction; hyperspectral imaging (HSI);
D O I
10.1109/LGRS.2005.846011
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Due to constraints both at the sensor and on the ground, dimension reduction is a common preprocessing step performed on many hyperspectral imaging datasets. However, this transformation is not necessarily done with the ultimate data exploitation task in mind-for example, target detection or ground cover classification. Indeed, theoretically speaking it is possible that a lossy operation such as dimension reduction might have a negative impact on detection performance. This notion is investigated experimentally using real-world hyperspectral imaging data. The popular principal components transform [aka. principal components analysis (PCA)] is used to explore the impact that dimension reduction has on adaptive detection of difficult targets in both the reflective and emissive regimes. Using seven state-of-the-art algorithms, it is shown that in many cases PCA can have a minimal impact on the detection statistic value for a target that is spectrally similar to the background against which it is sought.
引用
收藏
页码:192 / 195
页数:4
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