# A NOVEL ASYMMETRIC VARIANCE-BASED HYPOTHESIS TEST FOR A DIFFICULT SURVEILLANCE PROBLEM

### Dalton Rosario

#### Abstract

Local anomaly detectors have become quite popular for applications requiring hyperspectral (HS) target detection in natural clutter background assisted by an image analyst. Their popularity may be attributed to the simplicity of the algorithms designed to function as such. A disadvantage of using such detectors, however, is that they often produce an intolerable high number of detections per scene, which—according to image analysts—becomes a nuisance rather than an aiding tool. We present an effective local anomaly detector for HS data. The new detector exploits a notion of indirect comparison between two sets of samples and is free from distribution assumptions. The notion led us to derive a compact solution for a variance test, in which, under the null hypothesis, the detector’s performance converges to a known distribution. Experimental results using both simulated multivariate data and real HS data are presented to illustrate the effectiveness of this detector over five known alternative techniques.

#### References

- Casella, G., R. L. Berger, 1990. Statistical Inference. Duxbury Press. Belmont, CA.
- Manolakis, D., G. Shaw, 2002. Detection algorithms for hyperspectral imaging applications. In IEEE Signal Processing Magazine, pp 29. IEEE Press.
- Kwon H., S.Z. Der, and N.M. Nasrabadi, 2003. Adaptive anomaly detection using subspace separation for hyperspectral imgery, Opt. Eng., v. 42 (11), pp. 3342- 3351. OE Press.
- Schowengerdt, R., 1997. Remote Sensing, Models and Methods for Image Processing, Academic. San Diego, 2nd edition.
- Schweizer, S., J. M. F. Moura, 2000. Hyperspectral Imagery: Clutter Adaptation in Anomaly Detection. In IEEE Trans. Information Theory, vol. 46, no. 5, pp. 1855-1871. IEEE Press.
- Yu, X., L. Hoff, I. Reed, a. Chen, L. Stotts, 1997. Automatic target detection and recognition in multiband imagery. In IEEE Tran. Image Processing, vol. 6, pp. 143-156. IEEE Press.

#### Paper Citation

#### in Harvard Style

Rosario D. (2006). **A NOVEL ASYMMETRIC VARIANCE-BASED HYPOTHESIS TEST FOR A DIFFICULT SURVEILLANCE PROBLEM** . In *Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,* ISBN 972-8865-40-6, pages 277-284. DOI: 10.5220/0001360802770284

#### in Bibtex Style

@conference{visapp06,

author={Dalton Rosario},

title={A NOVEL ASYMMETRIC VARIANCE-BASED HYPOTHESIS TEST FOR A DIFFICULT SURVEILLANCE PROBLEM},

booktitle={Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,},

year={2006},

pages={277-284},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0001360802770284},

isbn={972-8865-40-6},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,

TI - A NOVEL ASYMMETRIC VARIANCE-BASED HYPOTHESIS TEST FOR A DIFFICULT SURVEILLANCE PROBLEM

SN - 972-8865-40-6

AU - Rosario D.

PY - 2006

SP - 277

EP - 284

DO - 10.5220/0001360802770284