number of anomalous pixels both actual and detected, the wrong 
detections and the number of not detected anomalous pixels. 
The number of the detected areas after filtering is wrong in the 
first case, due to misregistration. The number of pixels detected 
in the test 3 is almost correct, but the contemporary detection in 
both illuminated and shadowed zones is not performed 
automatically. 
4. CONCLUSIONS AND FUTURE WORKS 
A semiautomatic method for anomalous change detection has 
been described. The method uses the grouping of the image 
difference values, to detect both small and diffused changes. 
Three tests has been realized to evaluate the performances of the 
method. The results show that the better performances are 
obtained in case of cloudy days, while the contemporary 
presence of lighted and shadowed zones makes impossible an 
automatic detection. 
The foreseen development of the research will regard the 
isolation of single areas and the use of the procedure for images 
with noises due to, e.g., rain or fog. 
5. REFERENCES 
5.1 References from Journals: 
Cronk, S., Fraser, C., Hanley, H., 2006. Automated metric 
calibration of colour digital cameras. In: The Photogrammetric 
Record, 21 (116), pp. 355-372. 
Habib, A. F., Morgan, M.,, Lee, Y., 2002. Bundle adjustment 
with self-calibration using straight lines. The Photogrammetric 
Record, 17(100) pp. 635-650. 
Habib, A. F., Alruzouq, R.1., 2004. Line-based modified iterated 
Hough transform for automatic registration of multi-source 
imagery. The Photogrammetric Record, 19(105) pp. 5-21. 
Habib, A., Ghanma, M., Morgan, M. and Al-Ruzoug, R., 2005. 
Photogrammetric and lidar data registration using linear 
features. Photogrammetric Engineering & Remote Sensing, 
Vol. 71, No. 6, pp. 699-707. 
Heo, J., FitzHugh, T., 2000. A standardized radiometric 
normalization method for change detection using remotely 
sensed imagery. Photograrmmetric Engineering & Remote 
Sensing Vol. 66, No. 2 , pp. 173-181. 
Shannon, C.E, 1948. A mathematical theory of 
communication. Bell Systems Technical Journal, 27, pp. 379- 
423, 623-656. 
Theiler, J., 2008. Quantitative comparison of quadratic 
covariance-based anomalous change detectors. Applied Optics, 
47, pp. F12—F26. 
Theiler, J., Perkins, S., 2006. Proposed Framework for 
Anomalous Change Detection. In ICML Workshop on Machine 
Learning Algorithms for Surveillance and Event Detection, pp. 
7-14. 
Wohlberg, B., Theiler, J., 2009. Improved Change Detection 
With Local Co-Registration Adjustments. In Proc. WHISPERS 
Workshop on Hyperspectral Image and Signal Processing: 
Evolution in Remote Sensing, Grenoble France, pp. 1-4. 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
   
Wohlberg, B., Theiler, J., 2010. Symmetrized local co- 
registration optimization for anomalous change detection. In 
Proc. SPIE Computational Imaging VIII ,San Jose, California, 
USA, vol. 7533, pp. 753307-1 — 753307-10. 
Zhang C, Fraser CS (2007). Automated registration of high- 
resolution satellite images, The Photogrammetric Record, 
22(117): 75-87. 
Zitova, B. and Flusser, J. (2003). Image registration methods: a 
survey. Image and Vision Computing, 21(11), pp. 977—1000. 
5.2 References from Books: 
Jaehne, B. , 1989. Digitale Bildverarbeitung. Springer Verlag 
5.3 References from Other Literature: 
Arsigny, V., Commowick, O., Pennec, X., Ayache, N., 2006. A 
fast and log-euclidean polyaffine framework for locally affine 
registration. Report N° 5865, Institut National de Recherche en 
Informatique et en Automatique, Sophia Antipolis Cedex, 
France. 
Artese, G., 2007a. Automatic Rectification of Images Through 
Scale Independent Targets. In: International Archives of 
Photogrammetry and Remote Sensing, Padua, Vol. XXXVI, 5- 
C55:14-21 
Artese, G., 2007b. Detecting and Tracking Vehicles In a 
Roundabout. In: /nternational Archives of Photogrammetry and 
Remote Sensing, Padua, Vol. XXXVI, 5-C55:6-13 
Artese, G., Gencarelli, M., 2008. Real time monitoring of 
cultural heritage through camera phone digital images. In: 
International Archives of Photogrammetry and Remote Sensing, 
Beijing, Vol. XXXV- B5 V: 347 - 352. 
Canny, J. 1986. A Computational Approach To Edge 
Detection. In: [EEE Trans. Pattern Analysis and Machine 
Intelligence, 8:679-714, 1986. 
Forstner, W. and Gulch, E., 1987. A Fast Operator for Detection 
and Precise Location of Distinct Points Corners and Centres of 
Circular Features In: ISPRS Intercommission Workshop , 1987 
Interlaken, pp. 281-305. 
Forstner, W., 1984. Quality Assessment of Object Location and 
Point Transfer using Digital Image Correlation Techniques. In: 
International Archives of Photogrammetry and Remote Sensing, 
Rio de Janeiro, Vol. XXV, A3a:197-219 
Harris,C.G., Stephens, M.,1988. A Combined Corner and Edge 
Detector. In: Proc. 4th Alvey Vision Conf., Univ. Manchester, 
pp. 147-151. 
Moravec, H.P., 1979. Visual Mapping by a Robot Rover. In: 
International Joint Conference on Artificial Intelligence, pp. 
598-600.