Search

Space target recognition is of great importance for maintaining aerospace safety and national security. When observing a space target, owing to the low spatial resolution of ground-based observation equipment, each pixel in a hyperspectral image might represent a mixture of several different materials. Hyperspectral unmixing is a process used to extract the endmembers and their corresponding abundances from hyperspectral data. Unfortunately, most existing methods cannot make full use of the available spatial information data. The paper proposes a new local manifold sparse regularized unmixing model based on similarity regularized nonnegative matrix factorization (SRNMF). To exploit the spatial information of the vis-NIR (approximately 400-2500 nm) hyperspectral image of a space target, image segmentation is introduced to generate similar local regions. These local regions are generated adaptively, and pixels within each region have similar abundance sparseness. Simulation experiments validated the high efficiency and precision of the proposed algorithm, which should also be suitable for other spectral analysis applications. [ABSTRACT FROM AUTHOR]

The article discusses the use of P-spline signal regression (PSR) with net analyte signal (NAS) method in developing a highly precised quantitative calibration model. It mentions that the NAS approach is important to develop a basis coefficient vector through a projection matrix. According to the authors, the matrix is essential to compute and predict the outcome of NAS.

The article offers information on the usefulness of the visible-near infrared (vis-NIR) spectroscopy. It mentions that the said technology is capable of rectifying the leaf biochemical parameter estimation as well as in providing scattering impact analysis. Furthermore, it notes that the vis-NIR technology is able to describe errors caused by scattering in a quantitative manner.

The article discusses a study that examines the influence of repeating errors on the results of human blood glucose measurements using the near-infrared spectroscopic method. It states that the performance has been conducted on error removal via the orthogonal subtraction method (EROS) and partial least squares (PLS) to eliminate repeating errors and construct a calibration model. It also notes that repeating errors influence the prediction accuracy of human blood glucose measurement.

The article discusses on the study of human blood glucose measurements with the use of near-infrared (NIR) spectroscopic method. It cites that error removal via orthogonal subtraction method (EROS) and partial least squares (PLS) were used in elimination of repeating errors. It mentions that the use of average spectrum method on NIR non invasive blood glucose measurement successfully eradicated the repeating errors on prediction accuracy.