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The main objective of this study is to investigate suitable approaches to monitor the land infrastructure growth over a period of time using multimodality of remote sensing satellite images. Bi-temporal change detection method is unable to indicate the continuous change occurring over a long period of time and thus to achieve this purpose, synthetic aperture radar (SAR) and multispectral satellite images of same geographical region over a period of 2015 to 2018 are obtained and analyzed. SAR data from Sentinel-1 and multispectral image data from Sentinel-2 and Landsat-8 are used. Statistical composite hypothesis technique is used for estimating pixel-based change detection. The well-established likelihood ratio test (LRT) statistic is used for determining the pixel-wise change in a series of complex covariance matrices of multilooked polarimetric SAR data. In case of multispectral images, the approach used is to estimate a statistical model from series of multispectral image data over a long period of time, assuming there is no considerable change during that time period and then compare it with the multispectral image data obtained at a later time. The generalized likelihood ratio test (GLRT) is used to detect the target (changed pixel) from probabilistic estimated model of the corresponding background clutter (non-changed pixels). To minimize error due to co-registration, 8- neighborhood pixels around the pixel under test are also considered. There are different challenges in both the cases. SAR images have the advantage of being insensitive to atmospheric and light conditions, but it suffers the presence of speckle phenomenon. In case of multispectral, challenge is to get quite large number of datasets without cloud coverage in region of interest for multivariate distribution modelling. Due to imperfect modelling there will be high probability of false alarm. Co-registration is also an important criterion in multitemporal image analysis.
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