Multimodal change monitoring using multitemporal satellite images.

Abstract

The main objective of this study is to monitor the land infrastructure growth over a period of time using multimodality of remote sensing satellite images. In this project unsupervised change detection analysis using ITPCA (Iterated Principal Component Analysis) is presented to indicate the continuous change occurring over a long period of time. The change monitoring is pixel based and multitemporal. Co-registration is an important criteria in pixel based multitemporal image analysis. The minimization of co-registration error is addressed considering 8- neighborhood pixels. Comparison of results of ITPCA analysis with LRT (likelihood ratio test) and GLRT (generalized likelihood ratio test) methods used for SAR and MS (Multispectral) images respectively in earlier publications are also presented in this paper. The datasets of Sentinel-2 around 0-3 days of the acquisition of Sentinel-1 are used for multimodal image fusion. SAR and MS both have inherent advantages and disadvantages. 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 a large number of datasets without cloud coverage in region of interest for multivariate distribution modelling.