The spatial resolution of a hyperspectral image is often coarse because of the limitations of the imaging hardware. Super-resolution reconstruction (SRR) is a promising signal post-processing technique for hyperspectral image resolution enhancement. This paper proposes a maximum a posteriori (MAP) based multi-frame super-resolution algorithm for hyperspectral images. Principal component analysi...

The emerging astronomical technique known as wide-field spatiospectral interferometry can provide hyperspectral images with spatial resolutions that are unattainable with a single monolithic-aperture observatory. The theoretical groundwork for operation and data measurement is presented in full detail, including relevant coherence theory. We also discuss a data processing technique for recoveri...

An expert system developed for use with geologic materials (minerals) has been modified to more generally analyze spectra of all types of materials, including manmade materials, military targets, and background. The methodology requires reflectance spectra, but can also be extended to emissive measurements in the mid-wave infrared (MWIR) and long-wave infrared (LWIR). The spectra of known mater...

In this paper we explore the skill of hyperspectral reflectance measurements and vegetation indices (VIs) derived from these in estimating carbon dioxide (CO2) fluxes of grasslands. Hyperspectral reflectance data, CO2 fluxes and biophysical parameters were measured at three grassland sites located in European mountain regions using standardized protocols. The relationships between CO2 fluxes, e...

In this paper, we proposed a new semi-supervised multi-manifold learning method, called semisupervised sparse multi-manifold embedding (S3MME), for dimensionality reduction of hyperspectral image data. S3MME exploits both the labeled and unlabeled data to adaptively find neighbors of each sample from the same manifold by using an optimization program based on sparse representation, and naturall...

A methodology is proposed for extracting information on land cover based on hyperspectral reflectance data derived from satellite image, without supervising with ground truth. The reflectance percentage, being a characteristic feature of the ground object acts as an indirect guidance to the classification and hence the method is named semi-supervised classification. It is tried with IRS LISS IV...