A Review : CEST Method Based Analysis for the Detection of Damaged Buildings in Crisis Areas
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2013 by IJETT Journal|
|Year of Publication : 2013|
|Authors : Anupam Kumar , Manpreet Kaur|
Anupam Kumar , Manpreet Kaur. "A Review : CEST Method Based Analysis for the Detection of Damaged Buildings in Crisis Areas". International Journal of Engineering Trends and Technology (IJETT). V4(1):67-72 Jan 2013. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
This paper describes new combined method that consists of a cooperative approach of several different algorithms for automated change detection. Remote sensing data are primary sources extensively used for change detection in rec ent decades. Many change detection techniques have been developed. This paper summarizes and reviews these techniques. Previous literature has shown that image differencing, principal component analysis and post - classification comparison are the most commo n methods used for change detection. In recent years, CEST method spectral mixture analysis, artificial neural networks and integration of geographical information system and remote sensing data have become important techniques for change detection applica tions. Different change detection algorithms have their own merits and no single approach is optimal and applicable to all cases. In practice, different algorithms are often compared to find the best change detection results for a specific application. Res earch of change detection techniques is still an active topic and new techniques are needed to effectively use the increasingly diverse and complex remotely sensed data available or projected to be soon available from satellite and airborne sensors. This p aper is a comprehensive exploration of all the major change detection approaches implemented as found in the literature.
 A. Singh, “Digital ch ange detection techniques using remote - sensed data ,” Int. J. Remote Sens. , vol. 10, pp. 989 – 1003, Oct. 1989.
 D. Lu, P.Mausel,E. Brondízio, and E. Moran, “Change detection techniques,” Int. J. Remote Sens. , vol. 25, pp. 2365 – 2407, Dec. 2003.
 P. Coppin, I. Jonckheere, K. Nackaerts, B.Muys, and E. Lambin, “Digital change detection methods in ecosystem moni toring — A review,” Int. J. Remote Sens. , vol. 25, pp. 1565 – 1596, Sep. 2004.
 J. R. Jensen , Introductory Digital Image Processing: A Remote Sensing Perspective . Englewood Cliffs, NJ: Prentice - Hall, 2005.
 R. D. Macleod and R. G. Congalton, “A quantitat ive comparison of change - detection algorithms for monitoring eelgrass from remotely sensed data,” Photogramm . Eng. Remote Sens. , vol. 64, pp. 207 – 216, Mar. 1998.
 A. A. Nielsen, K. Conradsen, and J. J. Simpson, “Multivariate alteration detection (MAD) a nd MAF post - processing in multispectral, bitemporal image data: New approaches to change detection studies,” Remote Sens. Environ. , vol. 64, pp. 1 – 19, 1998.
 X. Dai and S. Khorram, “Remotely sensed change detection based on artificial neural networks,” Photogram m . Eng. Remote Sens. , vol. 65, pp. 1187 – 1194, Oct. 1999.
 E.O. Brigham , FFT Anwendungen . München, Germany: Oldenbourg Verlag, 1997.
[9 ] Manfred Ehlers , S.Klonus, D . and Tomowski , “Comparison Of Automated Change Detection,” Remote Sensing and Geoinformation , pp . 319 - 327 , 2011 .
ASTER , AVHRR, CEST, Change detection , edge detection , segmentation , TM