Processing hyperspectral images

Classical chemometric methods, that is, the classification and regression presented in Section 4.3.1, are also applied to hyperspectral images.However,XxYx X matrices have to be unfolded into (1 ) 1 matrices before processing. In other words, the three-way OOV array is unfolded into a classical two-way OV matrix. 

One final piece of advice the product in data processing is only as good as the informational content of the input data. The field of study we now call chemometrics began in earnest about three decades ago with application of statistical principles to chemical data. Hyperspectral images provide lots and lots of data, and chemometrics can be used to extract important information from these data however, it is still limited by the quality of the input data. 

Yao, H., Tian, L. and Noguchi, N. (2001) Hyperspectral imaging system optimization and image processing. ASAE Meeting Paper Number 01-1105, 30 July-1 August, Sacramento, CA. 

Manolakis, D., Marden, D., and Shaw, G., Hyperspectral image processing for automatic target detection applications, Lincoln Laboratory Journal, 14(1), 79-116 (2003)... 

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... 

Within 10 years of the first attempts to use NIR multispectral or hyperspectral imaging for food products, prodigious advances have been achieved such that NIR hyperspectral imaging is now commonly used in both laboratory and on-line applications. Yet, this outstanding technique continues to raise many questions that must be addressed before it is more widely accepted. Such questions relate not only to the hardware and data processing but also to specific issues related to NIR imaging. 

The main attraction of hyperspectral imaging is to obtain the complete picture of the sample. It has been reported that particle size or blending quality (spatial distribution characteristics) is as important to the performance of a pharmaceutical product as its chemical composition [5, 6]. Hence, by characterizing both the chemical and spatial composition of the sample, hyperspectral imaging can provide valuable insight that bridges the relationships between processing and perfor-... 

Gowen, A. A., O Donnell, C. R, Cullen, P. J., Downey, G., and Frias, J. M. (2007). Hyperspectral imaging—An emerging process analytical tool for food quality and safety control. Trends in Food Science Technology, 18(12), 590-598. 

In literature it s possible find several examples of apvplications of spectroscopac image analysis. Hyperspectral imaging could be used as critical control points of food processing to inspect for potential contaminants, defects or lesions. Their absence is essential for ensuring food quality and safety. In some case the application on-line was achieved. 

Long ago, Manne proposed two theorems that clearly stated when the true unique solution (C and profiles) of a resolution problem can be recovered based on the natural local rank conditions of the data set [129]. Although these conditions were originally meant for structured processes, they can be easily reformulated for hyperspectral image data analysis as follows ... 

Two examples will illustrate the advantages of multiset analysis in terms of the relevance of spectral consistency (and, therefore, definition of compounds) and on the advantages of using hyperspectral imaging toward classical spectroscopy in the modeling of complex processes (transformations). 

G. (2012) Hyperspectral imaging techniques applied to the monitoring of wine waste anaerobic digestion process. Opt. Eng., 51 (11), 111708. 

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