Hyperspectral imaging has attracted researchers’ interests in recent years. Because of its high dimensionality and complexity, the reduction of dimension of hyperspectral data sets has become crucial in processing and categorizing the data. In this project, I will apply two methods of dimension reduction: laplacian eigenmaps and randomized principal component analysis in order to reduce the dim...

Imaging spectroscopy, also known as hyperspectral imaging, is a new technique that has gained tremendous popularity in many research areas, including satellite imaging and aerial reconnaissance. In particular, NASA is continuously gathering high-dimensional image data from the surface of the earthwith hyperspectral sensors such as the Jet Propulsion Laboratory’s Airborne Visible-Infrared Imagin...

A hyperspectral image is a large dataset in which each pixel corresponds to a spectrum, thus providing high-quality detail of a sample surface. Hyperspectral images are thus characterised by dual information, spectral and spatial, which allows for the acquisition of both qualitative and quantitative information from a sample. A hyperspectral image, commonly known as a “hypercube”, comprises two...

Hyperspectral Imagery Sensing (HIS) is widely gained tremendous popularity in many research areas such as remotely sensed satellite imaging and aerial reconnaissance. HIS is an important technique with the measurement, analysis, and interpretation of spectra acquired sensing scene an airborne or satellite sensor. The development of sensor technology brought the developing of collecting image da...

anomaly detection (ad) has recently become an important application of target detection in hyperspectral images. the reed-xialoi (rx) is the most widely used ad algorithm that suffers from “small sample size” problem. the best solution for this problem is to use dimensionality reduction (dr) techniques as a pre-processing step for rx detector. using this method not only improves the detection p...

SUMMARY A hyperspectral camera has been acquired by the Department of Geography of the University of Alcalá. The system has been manufactured by Specim (www.specim.fi) at Oulu, Finland. The hyperspectral camera is composed of an ImSpector N17, a SU320-1.7TR NIR CCD camera, a spinning mirror installed in order to scan a 2 dimensional target and a fibre optic downwelling irradiance sensor (FODIS)...

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...