详情

原文传递 Autoencoded Reduced Clusters for Anomaly Detection Enrichment (ARCADE) in Hyperspectral Imagery.

题名：	Autoencoded Reduced Clusters for Anomaly Detection Enrichment (ARCADE) in Hyperspectral Imagery.
作者：	McLean, B. A.
关键词：	Hyperspectral imagery, Clustering, Multivariate analysis, Artificial neural networks, Feature extraction, Image processing. algorithms, Anomaly detection, Reconstructive error, Mahalanobis distance, Arcade (autoencoded reduced clusters for anomaly detection enrichment), His (hyperspectral imagery)
摘要：	Anomaly detection in hyper-spectral imagery is a relatively recent and important research area. The shear amount of data available in a many hyper-spectral images makes the utilization of multivariate statistical methods and artificial neural networks ideal for this analysis. Using HYDICE sensor hyperspectral images, we examine a variety of pre-processing techniques within a framework that allows for changing parameter settings and varying the methodological order of operations in order to enhance detection of anomalies within image data. By examining a variety of different options, we are able to gain significant insight into what makes anomaly detection viable for these images, as well as what impact parameter and methodology changes can have on the total classification effectiveness, false positive fraction and true positive fraction regarding classification.
报告类型：	科技报告