Hyperspectral Data Processing: Algorithm Design and Analysis

28.4 Computer Simulations Using AVIRIS Data

In order to demonstrate the utility of KFSCSP techniques in spectral estimation, identification, and quantification, computer simulations and real data experiments were conducted for performance evaluation and analysis. For computer simulations, five Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) reflectance data in Figure 1.8 are reproduced in Figure 28.1 for reference. There are five signature vectors, blackbrush, creosote leaves, dry grass, redsoil, and sagebrush, to be used for experiments where these spectra have 158 bands after water bands are removed.

Figure 28.1 Reflectances of creosote leaves, blackbrush, sagebrush, drygrass, and redsoil.

According to Figure 28.1, the signatures of blackbrush, creosote leaves, and sagebrush are close to each other in terms of spectral shape. In particular, the spectral signatures of creosote leaves and sagebrush are very similar. A detailed quantitative analysis of these three signatures can be found in Chang (2003a).

Since KFSCSP techniques are signature vector-based techniques not to be used for image pixel vectors, KFSSE, KFSSI, and KFSSQ are not designed for classification. Therefore, their performance is evaluated by signature vector-based spectral measures such as spectral angle mapper (SAM), spectral information divergence (SID) rather than image classifiers.

28.4.1 KFSSE

To implement ...