Hyperspectral imaging, due to providing high spectral resolution images, is one of the most important tools in the remote sensing field. Because of technological restrictions hyperspectral sensors has a limited spatial resolution. On the other hand panchromatic image has a better spatial resolution. Combining this information together can provide a better understanding of the target scene. Spec...

With the advent of remote sensing, the requirement of high resolution images that can provide high quality spectral and spatial information has increased. Due to cost and circuitry issues, the satellite sensors are capable of providing either multispectral (MS) images with high spectral and low spatial resolution or Panchromatic (pan) images with high spatial and low spectral resolution. Pansha...

A general framework of spatio-spectral segmentation for multispectral images is introduced in this paper. The method is based on classification-driven stochastic watershed by Monte Carlo simulations, and it gives more regular and reliable contours than standard watershed. The present approach is decomposed into several sequential steps. First, a dimensionality reduction stage is performed using...

This work describes numerical methods for the joint reconstruction and segmentation of spectral images taken by compressive sensing coded aperture snapshot spectral imagers (CASSI). In a snapshot, a CASSI captures a two-dimensional (2D) array of measurements that is an encoded representation of both spectral information and 2D spatial information of a scene, resulting in significant savings in ...

Hyper-spectral Image is a three dimensional array containing spatial (image) information on x & y axis and spectral information on z axis. It is high resolution imagery. Hyper-spectral image is a dynamic & powerful method to visualize data in terms of spatial & spectral features. It consumes a huge amount of storage. So due to large memory requirements, Hyperspectral images demand efficient com...

Hyperspectral sensors capture a portion of the visible and near-infrared spectrum with many narrow spectral bands. This makes it possible to better discriminate objects based on their reflectance spectra and to derive more detailed object properties. For technical reasons, the high spectral resolution comes at the cost of lower spatial resolution. To mitigate that problem, one may combine such ...

Hyperspectral images (HSIs) generally contain tens or even hundreds of spectral segments within a specific frequency range. Due to the limitations and cost imaging sensors, HSIs often trade spatial resolution for finer band resolution. To compensate loss maintain balance between space spectrum, existing algorithms were used obtain excellent results. However, these could not fully mine coupling ...

EPR spectroscopy can give biologically important information, such as tissue redox status, pO2, pH, and microviscosity, based on variation of EPR spectral characteristics (i.e., intensity, linewidth, hyperfine splitting, and spectral shape of free radical probes. EPR imaging (EPRI) can obtain 1D-3D spatial distribution of such spectral components using several combinations of magnetic field gra...