Image segmentation is a fundamental approach in the field of image processing and based on user’s application .This paper propose an original and simple segmentation strategy based on the EM approach that resolves many informatics problems about hyperspectral images which are observed by airborne sensors. In a first step, to simplify the input color textured image into a color image without tex...

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...

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 ...

With the development of sensor technology, the spectral resolution of remote sensing image is continuously improved. The appearance of the hyperspectral remote sensing is a tremendous leap in the field of remote sensing. The increasing availability of hyperspectral data and image has enriched us with better and finer data and it also enable us a much stronger ability to identify features. Howev...

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...

Hyperspectral data visualizations are useful as a background layer to labeling information in the hyperspectral scene such as classification information, locations, or geographic features. Given a hyperspectral image H , where the ith-jth pixel Hij is a d-dimensional vector representing reflectance at d wavelengths, any dimensionality-reduction method an be used to reduce the d dimensions down ...

Hyperspectral image mostly have very large amounts of data which makes the computational cost and subsequent classification task a difficult issue. Firstly, to solve the problem of computational complexity, spectral clustering algorithm is imported to select efficient bands for subsequent classification task. Secondly, due to lack of labeled training sample points, this paper proposes a new alg...