HYBRID LEARNING APPROACH FOR FEATURE EXTRACTION AND CLASSIFICATION IN HYPERSPECTRAL IMAGES

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...

a preprocessing stage before the feature extraction procedure in classification of hyperspectral images data

hyperspectral data potentially contain more information than multispectral data because of their higher spectral resolution. however, the stochastic data analysis approaches that have been successfully applied to multispectral data are not as effective for hyperspectral data as well. various investigations indicate that the key problem that causes poor performance in the stochastic approaches t...

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,...

Morphological memories for feature extraction in hyperspectral images

Improved Statistics Estimation and Feature Extraction for Hyperspectral Data Classification

....................................................................................................................vii CHAPTER 1: INTRODUCTION...................................................................................1 1.1 Statement of the Problem...............................................................................1 1.2 Organization of This Report ...............................