TY - GEN
T1 - Dimension reduction by random projection for endmember extraction
AU - He, Mingyi
AU - Mei, Shaohui
PY - 2010
Y1 - 2010
N2 - Random Projection (RP) has been proven to be a powerful technique for Dimension Reduction (DR). In this paper, it is applied to hyperspectral images as a DR preprocess step for Endmember Extraction (EE). Theoretical analysis demonstrates that RP can preserve geometric simplex fitting by hyperspectral data perfectly. Therefore, endmembers, which play an extremely important role for Spectral Mixture Analysis (SMA) of hyperspectral images, can be extracted from the projected data in a subspace by RP and the computational complexity of EE can be greatly reduced. Experimental results demonstrate that RP is computational efficient and data-independent DR technique for EE.
AB - Random Projection (RP) has been proven to be a powerful technique for Dimension Reduction (DR). In this paper, it is applied to hyperspectral images as a DR preprocess step for Endmember Extraction (EE). Theoretical analysis demonstrates that RP can preserve geometric simplex fitting by hyperspectral data perfectly. Therefore, endmembers, which play an extremely important role for Spectral Mixture Analysis (SMA) of hyperspectral images, can be extracted from the projected data in a subspace by RP and the computational complexity of EE can be greatly reduced. Experimental results demonstrate that RP is computational efficient and data-independent DR technique for EE.
UR - http://www.scopus.com/inward/record.url?scp=77956019861&partnerID=8YFLogxK
U2 - 10.1109/ICIEA.2010.5516724
DO - 10.1109/ICIEA.2010.5516724
M3 - 会议稿件
AN - SCOPUS:77956019861
SN - 9781424450466
T3 - Proceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010
SP - 2323
EP - 2327
BT - Proceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010
T2 - 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010
Y2 - 15 June 2010 through 17 June 2010
ER -