DocumentCode :
484142
Title :
A Parallel Approach for Initialization of High-Order Statistics Anomaly Detection in Hyperspectral Imagery
Author :
Ren, Hsuan ; Chang, Yang-Lang
Author_Institution :
Center for Space & Remote Sensing Res., Nat. Central Univ., Jhonli-Li
Volume :
2
fYear :
2008
fDate :
7-11 July 2008
Abstract :
Anomaly detection for remote sensing has drawn a lot of attention lately. An anomaly has distinct spectral features from its neighborhood, whose spectral signature is not known a priori, and it usually has small size with only a few pixels. It is very challenge to detect anomalies, especially without any information of the background environment in hyperspectral data with hundreds of co-registered image bands. Several methods are devoted to this problem, including the well-known RX algorithm which takes advantage of the second-order statistics and other algorithms which detect anomaly based on higher order statistics such as skewness and kurtosis. It has been proved that the High-Order Automatic Anomaly Detection Algorithm can outperform RX algorithm by distinguishing different types of anomalies. However, the initialization of the High-Order Automatic Anomaly Detection Algorithm remains a challenge problem. When the initial vectors are selected randomly for this recursive algorithm, they might be trapped in the local maximums and give different projection directions. But in our experiments, all those directions will show different types of anomalies. Therefore, this algorithm is particular suitable for parallel processing to increase the computing efficiency. In the parallel architecture, we will first randomly generate initial vectors for each process, and then united those output results for the orthogonal projection base. We will also compare the computational efficiency with the number of parallel processes we used.
Keywords :
geophysical signal processing; image processing; parallel processing; remote sensing; RX algorithm; co-registered image bands; computational efficiency; high-order automatic anomaly detection algorithm; high-order statistics; hyperspectral imagery; kurtosis; parallel processing; recursive algorithm; remote sensing; skewness; Clustering algorithms; Concurrent computing; Detection algorithms; Higher order statistics; Hyperspectral imaging; Hyperspectral sensors; Parallel architectures; Parallel processing; Remote sensing; Space technology; Hyperspectral; anomaly detection; high-order statistics; parallel process;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4244-2807-6
Electronic_ISBN :
978-1-4244-2808-3
Type :
conf
DOI :
10.1109/IGARSS.2008.4779170
Filename :
4779170
Link To Document :
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