Article history: Received 12 October 2014 Received in revised form 26 December 2014 Accepted 1 January 2015 Available online 25 February 2015

Hyperspectral imaging offers new opportunities for inter-person facial discrimination. However, due to the high dimensionality of hyperspectral data, discriminative feature extraction for face recognition is more challenging than 2D images. For dimensionality reduction and feature extraction most of the previous approaches just sub sampled the hyperspectral data [5, 6, 9] or used simple PCA [3]...

I n recent years, it has become clear that hyperspectral imaging has formed a core area within the geoscience and remote sensing community. Armed wi th advanced optical sensing technology, hyperspectral imaging offers high spectral resolution—a hyperspectral image can contain more than 200 spectral channels (rather than a few channels as in multispectral images), covering visible and near-infra...

“Compression of hyperspectral images holds importance because of bad transmission capability of satellites. The project is the implementation of the paper-“Satellite Hyperspectral Imagery Compression Algorithm Based on Adaptive Band Regrouping”. But during the course of the implementation many modifications have been made and some new concepts have been introduced. We have made the given regrou...

This dataset presents two series of hyperspectral images of healthy and infected apple tree leaves acquired daily, from two days after inoculation until an advanced stage of infection (11 days after inoculation). The hyperspectral images were calibrated by reflection correction and registered to match the geometry of one reference image. On the last experiment day, scab positions are provided.

The hyperspectral image enriches spectrum information, so compared with panchromatic image and multispectral image; it can classify the ground target better. The feature extraction of hyperspectral image is the necessary step of the ground target classification, and the kernel method is a new way to extract the nonlinear feature. In this paper, First the mathematical model of the generalized di...

This study proposes a novel method for multichannel image gray level co-occurrence matrix (GLCM) texture representation. It is well known that the standard procedure for the automatic extraction of GLCM textures is based on a mono-spectral image. In real applications, however, the GLCM texture feature extraction always refers to multi/hyperspectral images. The widely used strategy to deal with ...

Hyperspectral images have a higher spectral resolution (i.e., a larger number of bands covering the electromagnetic spectrum), but a lower spatial resolution with respect to multispectral or panchromatic acquisitions. For increasing the capabilities of the data in terms of utilization and interpretation, hyperspectral images having both high spectral and spatial resolution are desired. This can...

Hyperspectral data has become a critical tool for use by military analysts and planners. The capture of fine spectral information enables the generation of information products which could not be produced using traditional imaging means. The challenge facing hyperspectral technology, as an operational capability, is with conversion of the raw sensor data into a useful information product that i...