Remote sensing data fusion algorithm for super-resolution: multi-temporal case

Alexander Belov; Anna Denisova

doi:10.1117/12.2569653

26 August 2020 Remote sensing data fusion algorithm for super-resolution: multi-temporal case

Alexander Belov, Anna Denisova

Proceedings Volume 11524, Eighth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2020); 115241O (2020) https://doi.org/10.1117/12.2569653
Event: Eighth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2020), 2020, Paphos, Cyprus

Abstract

Super-resolution remote sensing data fusion aims to compose the output image with the higher spatial and spectral resolution from the lower resolution input ones captured for the same territory. The images used for fusion are usually multi-temporal. However, existing multi-temporal image fusion methods exploit only cloud-free images that might be difficult to obtain for some territories where the weather conditions are moderately cloudy during the observation period. In this paper, the clouds and their shadows are considered as scene distortions i.e. significant local changes in brightness caused by some opaque objects or their shadows partially overlapping the scene at the moment of image registration. Here, we propose a multi-temporal remote sensing data fusion method adapted to the dataset containing images partially occupied by scene distortions. The method is based on gradient descent optimization procedure with scene distortion elimination in each iteration. The experiments with the modeled data revealed that our method provides spatial and spectral super-resolution even for datasets including images with scene distortions. In comparison with the scenedistortion free case, the proposed method reduces a root mean square error of the resulting image from 2 to 4% on average in the case of the mixed data sets with few undistorted images (from two to six). The overall research has shown that in the case of lack of the data without scene distortions, additional partially distorted images can be used to obtain better fusion results.

Citation Download Citation

Alexander Belov and Anna Denisova "Remote sensing data fusion algorithm for super-resolution: multi-temporal case", Proc. SPIE 11524, Eighth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2020), 115241O (26 August 2020); https://doi.org/10.1117/12.2569653

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KEYWORDS

Image fusion

Data fusion

Super resolution

Remote sensing

Imaging systems

Image quality

Clouds

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