Subpixel anomalous change detection in remote sensing imagery

Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation

Volumn , Issue , 2008, Pages 165-168

  Theiler, James ^a  

^a LOS ALAMOS NATIONAL LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CHANGE DETECTION; CHANGE DETECTORS; MACHINE-LEARNING; NEW PARAMETERS; REMOTE SENSING IMAGERY; SUB PIXELS;

IMAGE ANALYSIS; PIXELS; REMOTE SENSING; SIGNAL DETECTION;

DETECTORS;

EID: 60749104377     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SSIAI.2008.4512311     Document Type: Conference Paper

References (11)

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2. A. Schaum and A. Stocker, "Subclutter target detection using sequences of thermal infrared multispectral imagery," Proc. SPIE, vol. 3071, pp. 12-22, 1997.
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8. T. Hastie, R. Tibshirani, and J. Friedman, Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer-Verlag, 2001, this anomaly detection approach is developed in Chapter 14.2.4, and neatly illustrated in Fig 14.3.
9. D. Tax and R. Duin, "Uniform object generation for optimizing one-class classifiers," J. Machine Learning Res., vol. 2, pp. 155-173, 2002.
10. I. Steinwart, D. Hush, and C. Scovel, 'Density level detection is classification," in Advances in Neural Information Processing Systems 17, L K. Saul, Y. Weiss, and L. Bottou, Eds. Cambridge, MA: MIT Press, 2005, pp. 1337-1344.
11. J. Theiler and S. Perkins, 'Resampling approach for anomalous change detection," Proc. SPIE, vol. 6565, pp. 6565-65, 2007.