摘要
Endmember extraction is a key step in the hyperspectral image analysis process. The kernel new simplex growing algorithm(KNSGA), recently developed as a nonlinear alternative to the simplex growing algorithm(SGA), has proven a promising endmember extraction technique. However, KNSGA still suffers from two issues limiting its application. First, its random initialization leads to inconsistency in final results; second, excessive computation is caused by the iterations of a simplex volume calculation. To solve the first issue, the spatial pixel purity index(SPPI) method is used in this study to extract the first endmember, eliminating the initialization dependence. A novel approach tackles the second issue by initially using a modified Cholesky factorization to decompose the volume matrix into triangular matrices, in order to avoid directly computing the determinant tautologically in the simplex volume formula. Theoretical analysis and experiments on both simulated and real spectral data demonstrate that the proposed algorithm significantly reduces computational complexity, and runs faster than the original algorithm.
Endmember extraction is a key step in the hyperspectral image analysis process. The kernel new simplex growing algorithm (KNSGA), recently developed as a nonlinear alternative to the simplex growing algorithm (SGA), has proven a promising endmember extraction technique. However, KNSGA still suffers from two issues limiting its application. First, its random initialization leads to inconsistency in final results; second, excessive computation is caused by the iterations of a simplex volume calculation. To solve the first issue, the spatial pixel purity index (SPPI) method is used in this study to extract the first endrnember, eliminating the initialization dependence. A novel approach tackles the second issue by initially using a modified Cholesky fac- torization to decompose the volume matrix into triangular matrices, in order to avoid directly computing the determinant tauto- logically in the simplex volume formula. Theoretical analysis and experiments on both simulated and real spectral data demonstrate that the proposed algorithm significantly reduces computational complexity, and runs faster than the original algorithm.
基金
Project supported by the Zhejiang Provincial Natural Science Foundation of China(Nos.LY13F020044 and LZ14F030004)
the National Natural Science Foundation of China(No.61571170)
关键词
量子细胞自动机
可逆电路
传统设计
奇偶校验
发生器
校验器
元胞自动机
低功率
Endmember extraction, Modified Cholesky factorization, Spatial pixel purity index (SPPI), New simplex growingalgorithm (NSGA), Kernel new simplex growing algorithm (KNSGA)