In the last few years, hyperspectral imaging technique has had a bright future for application on nondestructive detection of agricultural products. However, in experiment, there are usually dozens or even hundreds of hyperspectral images. It needs to spend lots of time in preprocessing all the hyperspectral data. Software ENVI is a kind of image processing software, although the users can defi...

Hyperspectral image compression has become increasingly important in data exploitation because of enormous data volumes and high redundancy provided by hundreds of contiguous spectral channels. Since a hyperspectral image can be viewed as a 3-dimensional (3D) image cube, many efforts have been devoted to extending 2D image compression techniques to perform 3D image compression on hyperspectral ...

The term hyperspectral is used to refer to spectra consisting of large number of narrow, contiguously spaced spectral bands. In the field of remote sensing, the term hyperspectral is also used interchangeably with other terms such as spectroscopy, spectrometry, spectroradiometry and rarely ultraspectral. Spectroscopy is a branch of physics concerned with the production, transmission, measuremen...

Image noise is generated unavoidably in the hyperspectral image acquision process and has a negative effect on subsequent image analysis. Therefore, it is necessary to perform image denoising for hyperspectral images. This paper proposes a cubic total variation (CTV) model by combining the 2-D total variation model for spatial domain with the 1-D total variation model for spectral domain, and t...

An efficient method and system for compressive sensing of hyperspectral data is presented. Compression efficiency is achieved by randomly encoding both the spatial and the spectral domains of the hyperspectral datacube. Separable sensing architecture is used to reduce the computational complexity associated with the compressive sensing of a large volume of data, which is typical of hyperspectra...

Airborne and space-borne acquired hyperspectral images are used to recognize objects and to classify materials on the surface of the earth. The state of the art compressor for lossless compression of hyperspectral images is the SLSQ compressor. In this paper we discuss hyperspectral image compression: we show how to visualize each band of an hyperspectral image and how this visualization sugges...

Hyperspectral images provide detailed spectral information with more than several hundred channels. On the other hand, the high dimensionality in hyperspectral images also causes to classification problems due to the huge ratio between the number of training samples and the features. In this paper, Lyapunov Exponents (LEs) are used to determine chaotic-type structure of EO1 Hyperion hyperspectr...

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...

The HYPXIM concept introduces the next hyperspectral space sensors generation with : enhanced spatial resolution from 15 m to 8 m, higher revisit frequency available for Security and Defence actors, miniaturization allowing a microsatellite to achieve a high-resolution hyperspectral low cost demonstration mission by 2018, a new TIR Hyperspectral capability, multi-sensors fusion products using o...

Hyperspectral remote sensing images are consisted of several hundreds of contiguous spectral bands that can provide very rich information and has the potential to differentiate land cover classes with similar spectral characteristics. LIDAR data gives detailed height information and thus can be used complementary with Hyperspectral data. In this work, a hyperspectral image is combined with LIDA...