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...

Content-Based Image Retrieval (CBIR) systems are powerful search tools in image databases that have been little applied to hyperspectral images. Relevance Feedback (RF) is an iterative process that uses machine learning techniques and user’s feedback to improve the CBIR systems performance. We pursued to expand previous research in hyperspectral CBIR systems built on dissimilarity functions def...

Hyperspectral data has become a critical tool for use by military analysts and planners. The capture of fine spectral information enables the generation of information products which could not be produced using traditional imaging means. The challenge facing hyperspectral technology, as an operational capability, is with conversion of the raw sensor data into a useful information product that i...

Existing hyperspectral imaging systems produce low spatial resolution images due to hardware constraints. We propose a sparse representation based approach for hyperspectral image super-resolution. The proposed approach first extracts distinct reflectance spectra of the scene from the available hyperspectral image. Then, the signal sparsity, non-negativity and the spatial structure in the scene...

Air-borne 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 Spectral oriented Least SQuares (SLSQ) compressor (see [1–7]). In this paper we discuss hyperspectral image compression: we show how to visualize each band of a hypersp...

Hyperspectral remote sensing increases the detectability of pixeland subpixel-sized targets by exploiting the finer detail in the spectral signatures. In this paper, we describe a new unsupervised algorithm for the detection of both full pixel and mixed pixel targets in hyperspectral imagery. The proposed method automatically resolves targets by using extended mathematical morphology operations...

Hyperspectral images are widely used in several real-life applications. In this paper, we investigate on the compression of hyperspectral images by considering different aspects, including the optimization of the computational complexity in order to allow implementations on limited hardware (i.e., hyperspectral sensors, etc.). We present an approach that relies on a three-dimensional predictive...