Hyperspectral imaging objectives

Hyperspectral images consist of spectra measured at each spatial location of the object. If each of the spectra measured with a 4K pixel camera consists of absorbance values at 1000 wavenumbers, the total number of data points would be 4096000. At a data resolution of 20 bits, this would round out to 80 M bits or 10 M bytes. As the number of camera pixels increases and the spatial resolution improves, the... 

In addition to differences in scattering, NIR lighting/imaging also allows advantage to be taken of more subtle chemical differences between objects, although in this case more sophisticated systems based on multispectral or hyperspectral imaging are required. 

As in this situation the objects are moving, the hyperspectral images are easily created line-by-Hne. However, due to the high speed of the conveyor belt, huge amounts of data are collected and must be processed consequently, companies endeavor to reduce the quantity of information, and hence often switch from... 

Nicolai et al. (2007) wrote a review about the applications of non-destructive measurement of fruit and vegetable quality. Measurement principles are compwed, and novel techniques (hyperspectral imaging) are reviewed. Special attention is p>aid to recent developments in portable systems. The problem of calibration transfer from one spectrophotometer to another is introduced, as well as techniques for calibration transfer. Chemometrics is an essential part of spectroscopy and the choice, of corrected techniques, is primary (linear or nonlinear regression, such as kernel-based methods are discussed). The principal objective of spectroscopy system applications in fruit and vegetables sector have focused on the nondestructive measurement of soluble solids content, texture, dry matter, acidity or disorders of fruit and vegetables, (root mean square error of prediction want to be achieved). 

In Heiden et al. (2012), the height information obtained from LiDAR data has been introduced into orthorectification in order to improve the geometric accuracy of urban objects in the higher-resolution hyperspectral image data. This permitted to eliminate the influence of the facades of the buildings in large field of view (FOV) of such sensors and to improve the accuracy of surface material mapping. 

Multivariate Image Analysis Strong and Weak Multiway Methods Strong and weak -way methods analyze 3D and 2D matrices, respectively. Hyperspectral data cube structure is described using chemometric vocabulary [17]. A two-way matrix, such as a classical NIR spectroscopy data set, has two modes object (matrix lines) and V variables (matrix columns). Hyperspectral data cubes possess two object modes and one variable mode and can be written as an OOV data array because of their two spatial directions. 

Consequently, within an industrial environment, hyperspectral NIR imaging has been used primarily to improve object detection previously carried out using RGB cameras and optical systems. Yet, the many other advantages of these systems make them highly suited to not only on-line analysis but also high-speed laboratory analyses for product grading. 

The final target of hyperspectral analysis is to correlate some feature of the individual objects in an image with some properties of interest. To achieve this target, multivariate data analysis is the key. 

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