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—Satellite remote sensing imagery has been used to track changes on the Earth surface for applications including, plantation monitoring, and urban database updating. To achieve this, different sensors have been investigated including optical, synthetic aperture radars (SAR) or multi-spectral sensors. Optical sensors provide high resolution images due to the involved wavelengths. As a consequence, huge databases of optical images are currently available. On the other hand, SAR images can be acquired even at night or under bad weather conditions and thus are more rapidly available in emergency situations. Since remote sensing images are commonly used to monitor the earth surface evolution, this surveillance can be conducted by detecting changes between images acquired at different times and possibly by different kinds of sensors. A representative case is when an optical image of a given area is available and a new image is acquired in an emergency situation (resulting from a natural disaster for instance) by a radar satellite. In such a case, images with heterogeneous properties have to be compared for change detection.

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R.Navinkumar, & S.Mangaiyarkarasi. (2017). Multivariate statistical model for change Detection in images with image classification 1 Mr.R.Navinkumar, 2 S.Mangaiyarkarasi 1 . International Journal of Intellectual Advancements and Research in Engineering Computations, 5(2), 1383–1386. Retrieved from