Radiometric Normalization of Multitemporal and Multisensor Remote Sensing Images Based on a Gaussian Mixture Model and Error Ellipse

Relative radiometric normalization is often required in time series analysis of satellite Earth observations such as land cover change detection. Normalization process reduces the radiometric differences caused by changes in the environmental conditions during the acquisition of multitemporal satellite images. In this paper, we proposed an efficient and automatic method based on Gaussian mixture model (GMM) to find a set of subjectively chosen invariant pixels. A linear model, based on Error Ellipse, was then adjusted to normalize the subject image. The proposed method involves two main steps; in the first step, invariant pixels, which are known as most probable unchanged pixels, were obtained by analyzing image differences estimated by GMMs. Then, these pixels were used to model the relationship between two multitemporal images. To evaluate the proposed method in real analysis scenarios, three multitemporal datasets acquired by different satellite sensors such as Ikonos, Quickbird, SuperView-1, and Worldview-2 were analyzed. These images were collected before and after the 2011's Japan and the 2004's Indonesia Tsunamis, and the 2017's Iran–Iraq earthquake. Experimental results demonstrated that the proposed method can considerably improve the radiometric variations between temporal images for change detection applications.