Landsat and Sentinel images are often used for routine monitoring of the Earth’s surface. The challenge in routine monitoring is to minimize the bias of surface reflectance from cross-sensor multitemporal images. A reflectance normalization is performed to reduce the reflectance bias caused by changes in the environmental conditions during the acquisition of multitemporal images
This research proposed an optimization method for the normalization results to reduce the linear inequalities of multi-temporal images. The method is called Successive over-relaxation. The proposed method involves two main steps; in the first step, the initial step, which normalizes all images to obtain the initial slope, intercepts, and the residual value. Then the looping step, repeats the normalization until the surface reflectance is consistent enough that indicated by minimum residual value. Four bias propagation networks were compared for cross-sensor multitemporal images including bus, tree, ring, and mesh network. In experiments, multitemporal cross-sensor images acquired from Sentinel-2 Multispectral Imager (MSI) and Landsat-8 Operational Land Imager (OLI) were tested. The results demonstrated that the surface reflectance inconsistency in multitemporal cross-sensor images can be significantly reduced by using the proposed normalization method through the mesh and ring normalization. Compared with standard atmospheric correction methods, the RMSE and correlation coefficients of the proposed method were improved until 60.80% for the RMSE and 8.34% for the correlations. Then compared with related methods, the improvement achieved 7.56% for RMSE and 1.19% for the correlations.
Finally, the proposed method can establish the consistency of the surface reflectance between cross-sensor images compared to the normalization method without applying successive over-relaxations. The qualitative analysis of the normalized results revealed that the results without applying successive over-relaxation were unable to overcome the image discontinuities in the mosaic images, whereas the proposed method can fix that issue.

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