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...

In this paper, we address the issue of hyperspectral pansharpening, which consists in fusing a (low spatial resolution) hyperspectral image HX and a (high spatial resolution) panchromatic image P to obtain a high spatial resolution hyperspectral image. The problem is addressed under a convex variational constrained formulation. The fit-to-P data term favors high resolution hyperspectral images ...

Mathematical morphology (MM) is a theory for spatial structure analysis that was established by introducing fundamental operators applied to two sets [1]. A set is processed by another one having a carefully selected shape and size, known as the structuring element (SE). In the context of image processing, the SE acts as a probe for extracting or suppressing specific structures of the image obj...

Although hyperspectral images provide abundant information about objects, their high dimensionality also substantially increases computational burden. Dimensionality reduction offers one approach to Hyperspectral Image (HSI) analysis. Currently, there are two methods to reduce the dimension, band selection and feature extraction. In this paper, we present a band selection method based on Indepe...

The hyperspectral imaging technique described in [12] leads to the interesting problem of reconstructing a three-dimensional data cube from measured data. This problem involves three separate steps in which we must estimate values of a function from values of its Fourier transform. Depending on which of the two functions involved at each step has bounded support, that is, is zero off of a bound...

A multiscale approach to hyperspectral image data analysis using fractal signatures was proposed and implemented in the Interactive Data Language (IDL). For 2-D hyperspectral curves, fractal signature measures the changes in curve length with changing scale. Using NASA’s Earth Observing-1 (EO-1) Hyperion image from a study area near Denton, Texas, USA, the capabilities of fractal signatures in ...

A technique of spatial-spectral quantization of hyperspectral images is introduced. Thus a quantized hyperspectral image is just summarized by K spectra which represent the spatial and spectral structures of the image. The proposed technique is based on α−connected components on a region adjacency graph. The main ingredient is a dissimilarity metric. In order to choose the metric that best fit ...

We address the two dominant dilemmas encountered in attempting to demonstrate real-time hyperspectral imaging: how to record a three-dimensional spectral data cube with a conventional two-dimensional detector array and how to most efficiently transmit the spectral data cube through the information bottleneck constituted by the detector’s limited space–bandwidth product. We have demonstrated a n...

We present a study on the the applicability of hyperspectral images to evaluate color filter array (CFA) design and the performance of demosaicking algorithms. The aim is to simulate a typical digital still camera processing pipe-line and to compare two different scenarios: evaluate the performance of demosaicking algorithms applied to raw camera RGB values before color rendering to sRGB, and e...