Reducing computational complexity in hyperspectral anomaly detection: a feature level fusion approach.

被引：4

作者：

Acito, N. ^{[1
]}

Corsini, G. ^{[1
]}

Diani, M. ^{[1
]}

Greco, M. ^{[1
]}

机构：

[1] Univ Pisa, Dipartimento Ingn Informaz, I-56122 Pisa, Italy

来源：

2006 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-8 | 2006年

关键词：

Anomaly detection; hyperspectral signal processing; computational load reduction;

D O I：

10.1109/IGARSS.2006.466

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

In this paper a new strategy aimed at reducing the computational complexity in hyperspectral anomaly detection is introduced. It is based on the fusion of the results obtained by applying the RX detector to the data measured by the different optical systems in the adopted hyperspectral sensor. Two feature level fusion criteria are derived and the computational complexity of each of them is evaluated. A comparison among the RX algorithm detection performance and the ones of the proposed anomaly detectors is provided by considering a data set acquired by an airborne hyperspectral sensor.

引用

页码：1804 / 1807

页数：4

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