Fast Registration of Remotely Sensed Images for Earthquake Damage Estimation
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Fast Registration of Remotely Sensed Images for Earthquake Damage Estimation Arash Abadpour,1 Shohreh Kasaei,2 and S. Mohsen Amiri2 1 Department 2 Department
of Mathematical Science, Sharif University of Technology, P.O. Box 11365-9415, Tehran, Iran of Computer Engineering, Sharif University of Technology, P.O. Box 11365-9517, Tehran, Iran
Received 13 February 2005; Revised 16 September 2005; Accepted 26 September 2005 Recommended for Publication by Stephen Marshall Analysis of the multispectral remotely sensed images of the areas destroyed by an earthquake is proved to be a helpful tool for destruction assessments. The performance of such methods is highly dependant on the preprocess that registers the two shots before and after an event. In this paper, we propose a new fast and reliable change detection method for remotely sensed images and analyze its performance. The experimental results show the efficiency of the proposed algorithm. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved.
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INTRODUCTION
In recent years, the spatial and spectral resolutions of remotely sensed sensors and the revisiting frequency of satellites have increased extensively. These developments have offered the possibility of addressing new applications of remote sensing in environmental monitoring. On the other hand, the officials are getting more and more aware of using multispectral remotely sensed images for regular and efficient control of the environment [1, 2]. Change detection of remotely sensed images can be viewed as a general case of a global motion estimation usually used in the video coding applications. However, the following should be noted. (i) In video coding applications, objects are likely to be presented in the next frame unless we have occlusions, newly appeared objects, or lightning changes, or when we deal with degraded images. But, in remote sensing applications for situations such as earthquake, we are faced with very severe situations in which large areas are likely to be totally destroyed. (ii) In video coding applications, the temporal rate is about 30 frames per second, and thus one can benefit from the existing high temporal redundancy between successive frames (when there is no shot change), while in remote sensing applications, the time interval between two captured multiband images can be considerably long resulting in a very low temporal redundancy. (iii) In video coding applications, the segmentation and motion estimation stages can in done in a crisp fashion, while
in remote sensing applications because of the different range of changes that might exist between two shots, the decisions should be made in a fuzzy fashion to take advantage of its membership style soft decisions. (iv) In remote sensing applications, the size and the number of the multispectral images are much higher than those in video sequences; and thus even after dimension reduction processes, we still need to have very fast algorithms. (v) In remote sensing applications, due to the geometrical changes in image capturing
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