RELATIVE RADIOMETRIC NORMALIZATION USING SEVERAL AUTOMATICALLY CHOSEN REFERENCE IMAGES FOR MULTI-SENSOR, MULTI-TEMPORAL SERIES

Abstract

We propose a method for the relative radiometric normalization of long, multi-sensor image time series. This allows to increase the revisit time under comparable conditions. Although the relative radiometric normalization is a well-studied problem in the remote sensing community, the availability of an increasing number of images gives rise to new problems. For example, given long series spanning several years, finding features that are maintained through the whole period of time becomes arduous. Instead, we propose in this paper to use automatically detected reference images chosen by maximization of a quality metric. For each image, two affine correction models are robustly estimated using random sample consensus, using the two closest reference images; the final correction is obtained by linear interpolation. For each pair of source and reference images, pseudo-invariant features are obtained using a similarity measure invariant to radiometric changes. A final tone-mapping step outputs the images in the standard 8-bits range. This method is illustrated by the fusion of time series of Sentinel-2 at correction levels 1C, 2A, and Landsat-8 images. By using only the atmospherically corrected Sentinel-2 L2A images as anchors, the full output series inherits this atmospheric correction.