This article deals with the issue of reducing the spectral dimension of a hyperspectral image using principal component analysis (PCA). To perform this dimensionality reduction, we propose the addition of spatial information in order to improve the features that are extracted. Several approaches proposed to add spatial information are discussed in this article. They are based on mathematical mo...

Many remote sensing applications require not only the land cover of extracted objects but also shape parameters of and structural relationships between areas of speciic landcover. Conventional sensors such as Landsat TM ooer suucient spectral resolution for land cover classiication. Their pixel size, however, is relatively high in relation to the observed objects, leading to a high percentage o...

In this paper, we propose a spectral-spatial feature extraction and classification framework based on artificial neuron network (ANN) in the context of hyperspectral imagery. With limited labeled samples, only spectral information is exploited for training and spatial context is integrated posteriorly at the testing stage. Taking advantage of recent advances in face recognition, a joint supervi...

We report a novel hyperspectral optical sensor capable of providing image information with four degrees of freedom (4D), i.e. volumetric spatial information and spectral information simultaneously, in real time. The imaging principle is based on the diffraction properties of volume holographic optical elements designed as spatial-spectral filters. The 4D imager can be configured to image object...

Agricultural land use mapping and change detection are important for environmental assessment and crop yield estimation. An effective mapping and change detection of crop land use requires large image coverage with sufficient resolutions in spatial, spectral and temporal dimensions. But few earth observation sensors can solely collect image data that meet all these requirements. The MODIS (Mode...

With the increased development of technology, it is necessary to retrieve information from multi source images in order to produce a high quality fused image with spatial and spectral information. Image Fusion is a process which allows the combination of the relevant information from a set of images into a single image where the resultant fused image will be more informative than any of the inp...

We report results from a field demonstration of a nonscanning high-speed imaging spectrometer [computed-tomography imaging spectrometer (CTIS)] capable of simultaneously recording spatial and spectral information about a rapidly changing scene. High-speed spectral imaging was demonstrated by collection of spectral and spatial snapshots of a missile in flight. This instrument is based on compute...