Because hyperspectral imagery is generally low resolution, it is possible for one pixel in the image to contain several materials. The process of determining the abundance of representative materials in a single pixel is called spectral unmixing. We discuss the L1 unmixing model and fast computational approaches based on Bregman iteration. We then use the unmixing information and Total Variatio...

Data simulation is widely used in remote sensing to produce imagery for a new sensor in the design stage, for scale issues of some special applications, or for testing of novel algorithms. Hyperspectral data could provide more abundant information than traditional multispectral data and thus greatly extend the range of remote sensing applications. Unfortunately, hyperspectral data are much more...

This paper presents a new application to exploit the capabilities of hyperspectral imagery as a visual supporting tool during surgeries. In order to enhance the visualization of regions affected with spilt blood, a hyperspectral imaging technique has been developed. We propose a neural network approach to nonlinearly combine the wavelengths of the spectrum in order to reduce the effect of spilt...

An important aspect of spectral image analysis is identification of materials present in the object or scene being imaged. Enabling technologies include image enhancement, segmentation and spectral trace recovery. Since multi-spectral or hyperspectral imagery is generally low resolution, it is possible for pixels in the image to contain several materials. Also, noise and blur can present signif...

A novel discriminative supervised neighborhood preserving embedding (DSNPE) method is proposed for feature extraction in classifying hyperspectral remote sensing imagery. DSNPE can preserve the local manifold structure and the neighborhood structure. What’s more, for each data point, DSNPE aims at pulling the neighboring points with the same class label towards it as near as possible, while sim...

Remotely sensed data is widely used in ecological applications because of its great advantages. These advantages are that the measures are being objective, repeatable and serve continuous data of the observed area opposed to traditional field survey based data collection (Zagajewskij et al. 2005). The remote observation of different vegetation species is important and quantitative and qualitati...