Recursive spectral similarity measure-based band selection for anomaly detection in hyperspectral imagery

Journal of Optics

Volumn 13, Issue 1, 2011, Pages

  He, Yuanlei ^a   Liu, Daizhi ^a   Yi, Shihua ^a  

^a XI'AN RESEARCH INSTITUTE OF HIGH TECHNOLOGY   (China)

Author keywords

Anomaly detection; Band selection; Hyperspectral imagery; Spectral similarity measure

Indexed keywords

IMAGE PROCESSING; REMOTE SENSING; SPECTROSCOPY;

ANOMALY-DETECTION ALGORITHMS; AUTOMATIC TARGET DETECTION AND CLASSIFICATION ALGORITHMS; BAND SELECTION; DETECTION PERFORMANCE; HYPER-SPECTRAL IMAGERIES; HYPERSPECTRAL SIGNATURES; RECURSIVE CALCULATIONS; SPECTRAL SIMILARITY;

ANOMALY DETECTION;

EID: 78651491196     PISSN: 20408978     EISSN: 20408986     Source Type: Journal    
DOI: 10.1088/2040-8978/13/1/015401     Document Type: Article

References (16)

1. Ren H and Chang C-I 1998 A computer-aided detection and classification method for concealed targets in hyperspectral imagery IEEE Int. Geoscience and Remote Sensing Symp. Proc. 2, pp 1016-8
2. Keshava N 2001 Best bands selection for detection in hyperspectral processing IEEE Int. Conf. on Acoustics, Speech, and Signal Processing 5, pp 3149-52
3. Du Q 2003 Band selection and its impact on target detection and classification in hyperspectral image analysis IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data pp 374-7
4. Diani M, Acito N, Greco M and Corsini G 2008 A New Band Selection Strategy for Target Detection in Hyperspectral Images (Lecture Notes in Computer Science 5179) (Berlin: Springer) pp 424-31
5. Chang C-I 2000 An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis IEEE Trans. Inf. Theory 46 1927-32
6. Du Y, Chang C-I and Ren H 2004 New hyperspectral similarity measure for spectral characterization Opt. Eng. 43 1777-86
7. Wang S and Chang C-I 2007 Variable-number variable-band selection for feature characterization in hyperspectral signatures IEEE Trans. Geosci. Remote Sens. 45 2979-92
8. Chang C-I and Du Q 2004 Estimation of number of spectrally distinct signal sources in hyperspectral imagery IEEE Trans. Geosci. Remote Sens. 42 608-19
9. Chang C-I and Wang S 2006 Constrained band selection for hyperspectral imagery IEEE Trans. Geosci. Remote Sens. 44 1575-85
10. Du Q and Yang H 2008 Similarity-based unsupervised band selection for hyperspectral image analysis IEEE Geosci. Remote Sens. Lett. 5 564-8
11. ul Haq I, Xu X and Shahzad A 2009 Band clustering and selection and decision fusion for target detection in hyperspectral imagery IEEE Int. Conf. on Acoustics, Speech, and Signal Processing 34, pp 1101-4
12. Kraut S, Scharf L and Butler R 2005 The adaptive coherence estimator: a uniformly most-powerful-invariant adaptive detection statistic IEEE Trans. Signal Process. 53 427-38
13. Ren H and Chang C-I 2003 Automatic spectral target recognition in hyperspectral imagery IEEE Trans. Aerosp. Electron. Syst. 39 1232-49
14. Du Q, Ren H and Chang C-I 2003 A comparative study for orthogonal subspace projection and constrained energy minimization IEEE Trans. Geosci. Remote Sens. 41 1525-9
15. Robila S A 2006 A class of detection filters for targets and anomalies in multispectral/hyperspectral imagery Proc. IEEE Int. Conf. on Computer Vision and Pattern Recognition Workshop pp 132-40
16. van der Meer F 2006 The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery Int. J. Appl. Earth Obs. Geoinf. 8 3-17