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28 January 2008 Image analysis of hyperspectral and multispectral data using projection pursuit
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Proceedings Volume 6809, Visualization and Data Analysis 2008; 68090D (2008)
Event: Electronic Imaging, 2008, San Jose, California, United States
Given recent advancements of modern hyperspectral (HS) sensors, the potential for information extraction has increased drastically given the continual improvements in spatial and spectral resolution. As a result, more sophisticated feature extraction and target detection (TD) algorithms are needed to improve the performance of the image analyst, whether computer-based or human. In this paper, a novel TD algorithm based on Projection Pursuit (PP) is proposed and implemented. PP is a well-known technique for dimensionality reduction in multi-band data sets without loss of any critical information. This technique highlights different features of interest in an image, thus improving and simplifying subsequent anomaly detection. The new target detection technique is based on a hybrid of PP and Reed_Xiaoli (RX) anomaly detector. In this study, the combining of PP with the RX detector (PPRX) adds some extra value to the standard RX detection technique and leads the development of a TD method that can be applied on hyperspectral/multispectral (MS) data sets. This novel technique, after being trained by using the Projection Index (PI) and a priori information of target of interest, utilizes RX detector to evaluate each potential projection. The main drawback of previously introduced PP methods such as those based on Information Divergence and Kurtosis/Skewness is that these techniques are sensitive to statistical outliers and cannot be used to highlight a specific target of interest. This study uses three data sets: (1) 4-band IKONOS multispectral data (2) 210-band HYDICE, and (3) 200-band simulated hyperspectral data set.
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Nilofar Azizi and Julian Meng "Image analysis of hyperspectral and multispectral data using projection pursuit", Proc. SPIE 6809, Visualization and Data Analysis 2008, 68090D (28 January 2008);

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