Hyperspectral Data Processing: Algorithm Design and Analysis

10.7 Synthetic Image Experiments

Once again the synthetic images described in Chapter 4 were used for experiments to evaluate the performance of the proposed REEAs. Scenarios TI1 and TE1 were not included in the experiments because there is no randomness caused by noise, in which case REEAs do not work in this scenario due to the fact that they are designed as random algorithms. Three component transform techniques, PCA, MNF and ICA, were used to perform DR to reduce the original data space to a reduced data space with data dimensionality determined by VD.

10.7.1 RPPI

The experiments of RPPI were implemented by 500 skewers on two data sets, a reduced data cube by DR and the original data without DR where the data dimensionality to be retained after DR was n_VD = 5 estimated by the NWHFC method with P_F = 10⁻⁴. Figures 10.1(a) and (b) and 10.2(a) and (b) show the results for TI2 and TI3 produced by PPI and RPPI, respectively, where pixel vectors in Figures 10.1(a) and 10.2(a) marked by open circles were extracted by PPI with PPI counts greater than zero and all pixel vectors in Figures 10.1(b) and 10.2(b) were those in the intersection of all runs by RPPI. As shown in Figures 10.1 and 10.2, the number of pixels extracted by RPPI as endmembers was significantly reduced with only less than 10 falsely alarmed endmembers in Figures 10.1(a) and 10.2(a) compared to a very large number of pixels extracted by PPI in Figures 10.1(a) and 10.2(a) as endmembers with hundreds of falsely alarmed ...