Hyperspectral Image Classification For Based BEMD Multivariate Gray Module
- Mr.Phatangare B.G
- Prof.Manojkumar
Keywords— Bidimensional empirical mode decomposition (BEMD), classification, genetic algorithm (GA), hyperspectral image, multivariate gray model (MGM), support vector machine (SVM).
Abstract
Hyperspectral imaging could be used to simultaneously obtain large amounts of spatial and spectral information on the objects being studied. This paper provides a comprehensive review on the recent development of hyperspectral imaging applications in food and food products. The potential and future work of hyperspectral imaging for food quality and safety control is also discussed First,focusing on evaluating the model coefficients and convolutionintegral, which are key elements in reducing the predictionerror of the GM(1,N), we replace the existing (composite)trapezoidal rule with (composite) Simpson rule. Bidimensional empirical mode decomposition
(BEMD) has been one of the core activities in image processing .due to its fully data-driven and self-adaptive nature. Hyperspectral imaging which combines imaging and spectroscopic technology is rapidly gaining ground as a non-destructive, real-time detection tool for food quality and safety assessment.
Mr.Phatangare B.G, Prof.Manojkumar. "Hyperspectral Image Classification For Based BEMD Multivariate Gray Module".INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH ISSN:2321-9939, Vol.4, Issue 4, pp.834-838, URL :https://rjwave.org/ijedr/papers/IJEDR1604124.pdf
Volume 4 Issue 4
Pages. 834-838
