Author :
Tongzhou, Zhao ; Yanli, Wang ; Haihui, Wang
Author_Institution :
Hubei Province Key Lab. of Intell. Robot, Wuhan Inst. of Technol., Wuhan, China
Abstract :
Notice of Violation of IEEE Publication Principles
???Image Fusion Based on Multi-scale Kalman Filtering???
by Tongzhou Zhao, Yanli Wang, Haihui Wang
in the Proceedings of the Second International Workshop on Education Technology and Computer Science, March 2010, pp. 207-215
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE???s Publication Principles.
This paper is a duplication of the original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:
???Radar Image Fusion by Multiscale Kalman Filtering???
by G. Simone, F.C. Morabito, A. Farina
in the Proceedings of the Third International Conference on Information Fusion, July 2000, pp. 10-17An image fusion algorithm, based on the Multiscale Kalman Filter (MKF), has been applied to combine remotely sensed data, acquired by radars having different resolutions and can improved information carried by each input image. The considered images have been acquired during the AIRSAR Mission and SIR-C/X-SAR Mission. The data have been co-registered to refer each pixel of each image to a common regular grid. The image fusion algorithm has been tested, and the merged images have been presented at different resolutions. A lineament detection algorithm based on the Hough transform, has been applied to the full resolution input data and to the full resolution merged data. The Golden Gate bridge has been detected in both images, but the computed probability of false alarm is lower in the case of the finest scale merged image than in the finest scale input image.- This fact demonstrates that the knowledge provided by the coarser resolution data has been transferred to the merged image, improving the performance of the lineament detection algorithm.
Keywords :
Hough transforms; bridges (structures); geophysical image processing; image fusion; image registration; probability; radar imaging; radar resolution; remote sensing; synthetic aperture radar; AIRSAR mission; Golden Gate bridge; Hough transform; SIR-C/X-SAR mission; false alarm probability; image fusion; image registration; lineament detection algorithm; multiscale Kalman filtering; radars; remotely sensed data; Bridges; Detection algorithms; Filtering; Image fusion; Image resolution; Kalman filters; Pixel; Radar imaging; Radar remote sensing; Testing; Hough Transform; Image fusion algorithm; Multiscale Kalman Filter; Radar image; Synthetic Aperture Radar;
