Unsupervised Change Detection For Satellite Images,Case Study Flood

The common use of images obtained daily by remote sensing currently provides detailed monitoring of the changes occurring on the earth's surface.These changes,such as floods,create spectral differences that can be distinguished by remote sensing images analysis.Regardless of the origin of the images and the type of surface change,the correct processing of these data means adopting a flexible,robust and possibly nonlinear method to calculate the complex statistical relationships that correctly characterize the pixels in the images.Many methods have been established in the area of change detection.In reality,detecting change is a process that necessitates cautious consideration of numerous features such as the nature of change detection problems,image processing presets,selection of appropriate variables and algorithms to solve the CD problem.Traditional change detection methods consume more time,manipulator dependent,and influenced by noise and/or complex spectral classes in an area.Change maps attained through these methods typically suffer from isolated changed pixels and present low accuracy.To this aim,unsupervised change detection is a completely automatic and unsupervised method for accurate binary detection of changes.This technique allows accurate mapping without any user intervention,resulting in particularly useful when standby and system reaction times are a critical obstacle to determine changes in two or more co-registered remote sensing images acquired at diverse period of times.The kernel k-means clustering procedure is used in order to cluster two groups of pixels associate to the 'change' also 'no change' classes.we provide an effective way to solve the two main challenges of these approaches: this method relies on three different steps:(A)initialization of the clustering,(B)estimation of the kernel function parameters and clustering,and(C)final assignment of the pixels to their classes.In order to assess the effectiveness of the Kkm technique for numerous remote sensing images and applications,two different datasets attained by Landsat TM are used.The results show the flexibility and efficiency of this automatic CD method for environmental change monitoring.