Hyperspectral imaging is a new technique in remote sensing that generates hundreds of images, at different wavelength channels, for the same area on the surface of the Earth. In recent years, several efforts have been directed towards the incorporation of high-performance computing systems and architectures into remote sensing missions. With the aim of providing an overview of current and new t...

The emergence of IR hyperspectral sensors in recent years enables their use in remote environmental monitoring of gaseous plumes. IR hyperspectral imaging combines the unique advantages of traditional remote sensing methods such as multispectral imagery and nonimaging Fourier transform infrared spectroscopy, while eliminating their drawbacks. The most significant improvement introduced by hyper...

Few studies have examined hyperspectral remote-sensing image classification with type-II fuzzy sets. This paper addresses image classification based on a hyperspectral remote-sensing technique using an improved interval type-II fuzzy c-means (IT2FCM*) approach. In this study, in contrast to other traditional fuzzy c-means-based approaches, the IT2FCM* algorithm considers the ranking of interval...

Restricted by technical and budget constraints, hyperspectral images (HSIs) are usually obtained with low spatial resolution. In order to improve the spatial resolution of a given hyperspectral image, a new spatial and spectral image fusion approach via pixel group based non-local sparse representation is proposed, which exploits the spectral sparsity and spectral non-local self-similarity of t...

and efficient solution to the unmixing problem. independence great discrimination ability on unlike signatures, giving a robust The model has great noise robustness, a correlation rate and endmember number always low Error Ratios for all cases. components, endmember number and proportion on the mixture, providing network behaviour vs. the Signal-to-Noise Ratio, correlation rate between order to...

The problem of unsupervised learning and segmentation of hyperspectral images is a significant challenge in remote sensing. The high dimensionality of hyperspectral data, presence of substantial noise, and overlap of classes all contribute to the difficulty of automatically clustering and segmenting hyperspectral images. In this article, we propose an unsupervised learning technique that combin...

This report introduces a novel sparse decomposition model for hyperspectral image reconstruction. The model integrates two well-known sparse structures of hyperspectral images: a small set of signature spectral vectors span all spectral vectors (one at each pixel), and like a standard image, a hyperspectral image is spatially redundant. In our model, a threedimensional hyperspectral cube X is f...

PURPOSE To evaluate a hyperspectral imaging technique for monitoring relative spatial changes in retinal oxygen saturation. METHODS The optic nerve head (ONH) and overlying vessels in cynomolgus monkey eyes were imaged with a fundus camera attached to a hyperspectral imaging system. Images were acquired with inspiration of room air and pure oxygen and at controlled intraocular pressures (IOP)...

The amount of noise included in a hyperspectral image limits its application and has a negative impact on hyperspectral image classification, unmixing, target detection, and so on. In hyperspectral images, because the noise intensity in different bands is different, to better suppress the noise in the high-noise-intensity bands and preserve the detailed information in the low-noise-intensity ba...

With recent technological advances in remote sensing sensors and systems, very highdimensional hyperspectral data are available for a better discrimination among different complex landcover classes. However, the large number of spectral bands, but limited availability of training samples creates the problem of Hughes phenomenon or ‘curse of dimensionality’ in hyperspectral data sets. Moreover, ...