A hyperspectral imaging system (HSIS) is a combination of an imaging device and a spectrometer. The result is a 2D image combined with the third dimension containing the spectral composition of each pixel of the image. Spectrometers normally implemented in hyperspectral imaging systems are made by integrating a dispersive means (a prism or a grating) in an optical system, with the drawback of h...

The spatial resolution of hyperspectral image is often low due to the limitation of the imaging spectrometer. Fusing the original hyperspectral image with high-spatialresolution panchromatic image is an effective approach to enhance the resolution of hyperspectral image. However, it is hard to preserve the spectral information at the same time of enhancing the resolution by the traditional fusi...

This paper outlines the design and implementation of Grid-HSI, a Service Oriented Architecturebased Grid application to enable hyperspectral imaging analysis. Grid-HSI provides users with a transparent interface to access computational resources and perform remotely hyperspectral imaging analysis through a set of Grid services. Keyword: Grid computing, portal grid, remote sensing, image process...

Few studies have examined hyperspectral remote-sensing image classification with type-II fuzzy sets. This paper addresses image classification based on a hyperspectral remote-sensing technique using an improved interval type-II fuzzy c-means (IT2FCM*) approach. In this study, in contrast to other traditional fuzzy c-means-based approaches, the IT2FCM* algorithm considers the ranking of interval...

In this paper we study the application of bayesian network models to classify multispectral and hyperspectral remote sensing images. Different models of bayesian networks as: Naive Bayes, Tree Augmented Naive Bayes, Forest Augmented Naive Bayes and General Bayesian Networks, are applied in the classification of hyperspectral data. In addition, several bayesian multi-net models are applied in th...

Hyperspectral sensors provide hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. Since different materials show different spectral properties, hyperspectral imagery is an effective technology for accurately discriminating and classifying materials. However, issues such as the high dimensionality of the data and the presence of nois...

Hyperspectral imaging offers new opportunities for inter-person facial discrimination. However, compact and discriminative feature extraction from high dimensional hyperspectral image cubes is a challenging task. We propose a spatio-spectral feature extraction method based on the 3D Discrete Cosine Transform (3D-DCT). The 3D-DCT optimally compacts information in the low frequency coefficients. ...

Hyperspectral remote sensing technique provides fine and detailed spectral information by contiguous and narrow spectral channels. For the traditional classification algorithms, most of them are based on spectral information; spatial information which is useful for the hyperspectral data analysis is paid a little attention to. So, hyperspectral image classification based on effective combinatio...

Parametric geocoding is a standard procedure for orthorectification of hyperspectral airborne scanner data. Boresight parameters are estimated by using the image coordinates of precisely known ground reference points. To fully automate the processing of the recorded hyperspectral data ground reference points should be selected and matched by an algorithm without user interaction. In this articl...