Nonlinear Feature Extraction for Hyperspectral Images

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,...

Feature extraction of hyperspectral images using boundary semi-labeled samples and hybrid criterion

Feature extraction is a very important preprocessing step for classification of hyperspectral images. The linear discriminant analysis (LDA) method fails to work in small sample size situations. Moreover, LDA has poor efficiency for non-Gaussian data. LDA is optimized by a global criterion. Thus, it is not sufficiently flexible to cope with the multi-modal distributed data. We propose a new fea...

Morphological memories for feature extraction in hyperspectral images

Feature Extraction of Hyperspectral Images Using Matching Pursuit

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 ...

Overlap-based feature weighting: The feature extraction of Hyperspectral remote sensing imagery

Hyperspectral sensors provide a large number of spectral bands. This massive and complex data structure of hyperspectral images presents a challenge to traditional data processing techniques. Therefore, reducing the dimensionality of hyperspectral images without losing important information is a very important issue for the remote sensing community. We propose to use overlap-based feature weigh...