IFGF Based Feature Extraction of Hyperspectral Images

Hyperspectral sensors collect information as a set of images represented by different bands. Hyperspectral images are threedimensional images with sometimes over 100 bands where as regular images have only three bands: red, green and blue. Each pixel has a hyperspectral signature that represents different materials. Hyperspectral images can be used for geology, forestry and agriculture mapping, land cover analysis and atmospheric analysis. Even though hyperspectral images can sometimes contain over 100 bands, relatively few bands can explain the vast majority of the information. Hyperspectral images contain rich and fine spectral information, an improvement of land use/cover classification accuracy is expected from the use of such images. However, the classification methods that have been successfully applied to multispectral data in the past are not as effective as to hyperspectral data. The major cause is that the size of training data set does not correspond to the increase of dimensionality of hyperspectral data. Actually, the problem of the “curse of dimensionality” emerges when a statistic based classification method is applied to the hyperspectral data. For such reason, hyperspectral images are mapped into a lower dimension while preserving the main features of the original data by a process called dimensional reduction. This can be done by feature extraction that a small number of salient features are extracted from the hyperspectral data when confronted with a limited set of training samples.