This article presents a number of issues that are related to the hyperspectral sensors which are part of new technologies for the acquisition of images in satellite and aerial remote sensing. Hyperspectral sensors are characterized by the fact that they produce records in a large number of adjacent and narrow lanes thereby providing a very high spectral resolution. In this way, interpretations ...

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 article presents highlights of the research work done in hyperspectral remote sensing in the Himalayan cryosphere. Hyperspectral radiometric investigations conducted at different field locations of NW Himalaya and cold laboratory are discussed. Spectral signatures were collected for varying snow grain size, contamination, liquid water content, vegetation/soilmixed snow, glacier ice, morain...

Kernel Principal Component Analysis (KPCA) is investigated for feature extraction from hyperspectral remote-sensing data. Features extracted using KPCA are classified using linear support vector machines. In one experiment it is shown that kernel principal component features are more linearly separable than features extracted with conventional principal component analysis. In a second experimen...

Information about the distribution of grass nitrogen (N) concentration is crucial in understanding rangeland vitality and facilitates effective management of wildlife and livestock. A challenge in estimating grass N concentration using remote sensing in savannah ecosystems is that these areas are characterised by heterogeneity in edaphic, topographic and climatic factors. The objective is to te...

This paper evaluates the impact of removing random noise of radiance data using a spectral-spatial smoothing approach on data compression onboard a hyperspectral satellite. A datacube acquired using a Short Wave Infrared Full Spectrum Imager II for target detection application of hyperspectral data was tested. The impact was evaluated using both the statistical based measures and a remote sensi...

Hyperspectral images (HSI) classification is a high technical remote sensing software. The purpose is to reproduce a thematic map . The HSI contains more than a hundred hyperspectral measures, as bands (or simply images), of the concerned region. They are taken at neighbors frequencies. Unfortunately, some bands are redundant features, others are noisily measured, and the high dimensionality of...

Hyperspectral images have the high spectral resolution rather than to multispectral images. By development of remote sensing technology, the new sensors with hyperspectral capabilities in RS science will be replaced to multispectral imaging. A big advantage of hyperspectral images comparison to that of multispectral images is a continuous spectrum for each image cell that can be derived from im...

Undirected graphical models have been successfully used to jointly model the spatial and the spectral dependencies in earth observing hyperspectral images. They produce less noisy, smooth, and spatially coherent land cover maps and give top accuracies on many datasets. Moreover, they can easily be combined with other state-of-the-art approaches, such as deep learning. This has made them an esse...