Thee rapid advances in hyperspectral sensing technology have made it possible to collect remote sensing data in hundreds of bands. However, the data analysis methods which have been successfully applied to multispectral data are often limited to achieve satisfactory results for hyperspectral data. The major problem is the high dimensionality, which deteriorates the classification due to the Hughes phenomenon. In order to avoid this problem a feature reduction process is inevitable. There are currently many different methods for feature reduction process in hyperspectral data. The feature selection methods pick the most informative features and discard the redundant features from the total set of features. Feature extraction methods, on the other hand, transform a large amount of information into a small number of transformed features. The decision boundary feature extraction (DBFE) and nonparametric weighted feature extraction method (NWFE) are two important approaches for feature extraction. Another group of feature reduction algorithms are based on the theory of multiple classifiers. Thus far, many different methods for the feature reduction process have been proposed but the validation of these algorithms has not yet been done on an appropriate image dataset. The main goal of this study is to have a good evaluation of these different feature reduction algorithms based on a laboratorial hyperspectral data. Selection of classes for the simulated target was based on the challenging point of different algorithms which are classifying targets with very similar spectral characteristics, targets with different shapes, targets with high different spectral characteristics or targets with high spatial variability. In this respect following the aforesaid criteria 22 classes were considered in the final simulated target. The feature reduction methods were compared using the test image. The consistency between the various methods is discussed as

A spatial classification technique incorporating a novel feature derivation method is proposed for classifying the heterogeneous classes present in the hyper spectral images. The classification accuracy can be improved if and only if both the feature extraction and classifier selection are proper. As the classes present in the hyper spectral image are having different textures, textural classification is entertained. Wavelet based textural features extraction is entailed. Hyper spectral images are having dozen numbers of bands. Few mutually distinct bands are selected and wavelet transform is applied. For all the sub bands Gray Level Co-occurrence Matrix (GLCM) are calculated. From GLCMs co-occurrence features are derived for individual pixels. Apart from Co-occurrence features, statistical features are also calculated. Addition of statistical and co-occurrence features of individual pixels at individual bands form New Features for that pixel. By the process of adding these New Features of approximation band and individual sub-bands at the pixel level, Combined Features are derived. These Combined Features are used for classification. Support Vector Machines with Binary Hierarchical Tree (BHT) is developed to classify the data by One Against All (OAA) methodology. Airborne Visible Infra Red Imaging Sensor (AVIRIS) image of Cuprite –Nevada field is inducted for the experiment.

Constructing ensembles of classifiers using linear projections based on misclassified instances

Hyperspectral images provide abundant information about objects. The high dimensionality of such images arise various problems such as curse of dimensionality and large hypothesis space. There are two methods to overcome the high dimensionality problem which are band selection and feature extraction. In this paper we present a feature extraction method based on an angular criterion; this method is defined so that minimizes angle between mean vector and samples with in each class and maximizes the angle between mean classes and simultaneously satisfies fisher criterion. It explores other aspects of pattern in feature space and tries to discriminate classes with respect to geometric parameters. We have employed the angular and the fisher criteria for feature extraction also the spectral angle mapper (SAM) and minimum distance (MD) classifiers are used for image classification. The results demonstrate that this method can improve the discrimination of objects in feature space and improve the classification accuracy of SAM classifier. 2.