Hyperspectral remote sensing image is also known as an “Imaging Spectrometry” emerged as a promising technology for detection and identification of minerals, terrestrial vegetation, man-made materials and backgrounds. The word “Hyperspectral” is used to distinguish sensors with many tens or hundreds of bands from the more traditional multiple sensors. The success of a hyperspectral remote sensi...

This paper examines the utility of hyperspectral remote sensing to detect fresh and dry biomass, water content, and plant area index of burned and unburned grassland in southern California. Contrary to many previously published reports, the Normalized Difference Vegetation Index (NDVI, a vegetation greenness index) was not a good indicator of any of these important biophysical properties in eit...

Recent advances in sensor technology opened new possibilities for remote sensing. For example, the appearance of sensor higher spatial and spectral resolution. In terms of spectral resolution, the number of available bands increased significantly, resulting in hyperspectral sensors. Hyperspectral remote sensing images are characterized by the division of the electromagnetic spectrum in a great ...

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Hyperspectral remote sensing increases the detectability of pixeland subpixel-sized targets by exploiting the finer detail in the spectral signatures. In this paper, we describe a new unsupervised algorithm for the detection of both full pixel and mixed pixel targets in hyperspectral imagery. The proposed method automatically resolves targets by using extended mathematical morphology operations...

This paper presents a hyperspectral remote sensing image analysis frame based on homogeneous-regions. In this frame, a multi-scale segmentation method based on Spectral Code Mapping (SCM) was proposed, and spatial relationship was discussed for improved classification.

Remote sensing data are attractive for deriving land cover information through image classification. A number of parametric and non-parametric classifiers such as the maximum likelihood classifier (MLC) and the artificial neural network (ANN) have been developed and tested successfully on multispectral data. However, the existing classifiers have shown marked limitations in the classification o...