anomaly detection (ad) has recently become an important application of hyperspectral images analysis. the goal of these algorithms is to find the objects in the image scene which are anomalous in comparison to their surrounding background. one way to improve the performance and runtime of these algorithms is to use dimensionality reduction (dr) techniques. this paper evaluates the effect of thr...

The paper presents an experimental research of proposed method of atmospheric correction of hyperspectral remote sensing data. The method is based on approximate solution of MODTRAN transmittance equation using simultaneous analysis of remote sensing hyperspectral image and an ideal hyperspectral image of the same territory. Results of experimental research imitating conditions of practical met...

Resulting from the combination of Raman spectroscopy and optical microscopy, Raman hyperspectral imaging has proven to be an indispensable tool in the pharmaceutical field. This article will broach a number of Raman hyperspectral imaging applications that were developed in our laboratory, in order to demonstrate the significance of the technique. Raman Spectroscopy Raman Hyperspectral Imaging: ...

Hyperspectral unmixing (HU) is a very useful and increasingly popular preprocessing step for a wide range of hyperspectral applications. However, the HU research has been constrained a lot by three factors: (a) the number of hyperspectral images (especially the ones with ground truths) are very limited; (b) the ground truths of most hyperspectral images are not shared on the web, which may caus...

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...