The Relationship Between Detection Algorithms for Hyperspectral and Radar Applications
Abstract : Hyperspectral data consists of hundreds of contiguous radiometric measurements collected passively from each pixel in a scene. Detection capitalizes on exploiting the difference between target and background spectral signatures. Many detection methods in hyperspectral processing employ signal models commonly used in radar even though it is an active sensor. Starting from a common signal model, we discuss adaptive detection algorithms for hyperspectral data by outlining fundamental similarities and differences with radar. We demonstrate detection using hyperspectral data through experiments with real data and discuss the fundamental applicability of adaptive radar signal models to detection in hyperspectral processing.
OBJECTIVE ::: To establish a new method to discriminate Aaron's beard by Fourier transformation infrared spectrometry. ::: ::: ::: METHODS ::: Attenuation Reflection-Fourier transformation infrared spectrometry with clustering analysis was used to the identification of Aaron's beard. ::: ::: ::: RESULTS ::: There were obvious differences in Aaron's beard. The results are consistent with that of morphologic study. ::: ::: ::: CONCLUSION ::: This method is rapid, simple and economical, and can be used to the quality control.