Hyperspectral Data Processing: Algorithm Design and Analysis

4.4 Applications

The usefulness of the synthetic image-simulated six scenarios three applications are presented for illustration.

4.4.1 Endmember Extraction

Endmember extraction has received considerable interest in recent years and is probably one of the most important and crucial steps in hyperspectral image analysis since endmembers provide unique spectral information that is very valuable for data exploitation. Many algorithms have been developed and reported in the literature. Two most popular and widely used endmember extraction algorithms, pixel purity index (PPI) (Boardman, 1994) and N-finder algorithm (N-FINDR) (Winter, 1999a,b) with details in Chapter 7, were used for evaluation by the six designed scenarios. Since there are only five pure signatures, which are A, B, C, K, and M, dimensionality reduction required for PPI and N-FINDR was performed by the maximum noise fraction (MNF) transform (Green et al., 1998) to reduce the original data space to five dimensions. The results produced by the PPI using 500 skewers and N-FINDR are shown in Figures 4.10 and 4.11, respectively, where all pixels with PPI counts greater than zero are shown and marked by yellow pixels. Since there is no noise in TI1 and TE1, PCA instead of MNF was performed for dimensionality reduction.

Figure 4.10 Endmember extraction by PPI.

Figure 4.11 Five endmembers extracted by N-FINDR.

According to ...