Characterization of anomaly detection in hyperspectral imagery

被引：3

作者：

Chang, Chein-I ^{[1
]}

Hsueh, Mingkai ^{[1
]}

机构：

[1] Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore, MD

来源：

Sensor Review | 2006年 / 26卷 / 02期

关键词：

Correlation analysis; Differential geometry; Image processing;

D O I：

10.1108/02602280610652730

中图分类号：

学科分类号：

摘要：

Purpose - The paper aims to characterize anomaly detection in hyperspectral imagery. Design/methodology/approach - This paper develops an adaptive causal anomaly detector (ACAD) to investigate several issues encountered in hyperspectral image analysis which have not been addressed in the past. It also designs extensive synthetic image-based computer simulations and real image experiments to substantiate the work proposed in this paper. Findings - This paper developed an ACAD and custom-designed computer simulations and real image experiments to successfully address several issues in characterizing anomalies for detection, which are - first, how large size for a target to be considered as an anomaly? Second, how an anomaly responds to its proximity? Third, how sensitive for an anomaly to noise? Finally, how different anomalies to be detected? Additionally, it also demonstrated that the proposed ACAD can be implemented in real time processing and implementation. Originality/value - This paper is the first work on investigation of several issues related to anomaly detection in hyperspectral imagery via extensive synthetic image-based computer simulations and real image experiments. In addition, it also develops a new developed an ACAD to address these issues and substantiate its performance.

引用

页码：137 / 146

页数：9

共 8 条

[1] Chang C.-I., Hyperspectral Imaging: Techniques for Spectral Detection and Classification, (2003)
[2] Chang C.-I., Orthogonal subspace projection revisited: A comprehensive study and analysis, IEEE Trans. on Geoscience and Remote Sensing, 43, 3, pp. 502-518, (2005)
[3] Chang C.-I., Chiang S.-S., Anomaly detection and classification for hyperspectral imagery, IEEE Trans. on Geoscience and Remote Sensing, 40, 6, pp. 1314-1325, (2002)
[4] Harsanyi J.C., Chang C.-I., Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach, IEEE Trans. on Geoscience and Remote Sensing, 32, 4, pp. 779-785, (1994)
[5] Hsueh M., Adaptive Causal Anomaly Detection, (2004)
[6] Hsueh M., Chang C.-I., Adaptive Causal Anomaly Detection for Hyperspectral Imagery, (2004)
[7] Kwon H., Der S.Z., Nasrabadi N.M., Adaptive anomaly detection using subspace separation for hyperspectral imagery, Optical Engineering, 42, 11, pp. 3342-3351, (2003)
[8] Reed I.S., Yu X., Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution, IEEE Trans. on Acoustic, Speech and Signal Processing, 38, 10, pp. 1760-1770, (1990)

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