METHODS OF SPECTRAL MATCHING OF HETEROGENOUS REMOTE SENSING IMAGES
A.N. Borisov, V.V. Myasnikov, V.V. Sergeev
Samara University, Samara, Russia
Keywords: image registration, remote sensing, Landsat-8, Sentinel-2, principal component analysis, neural networks
Abstract
In this paper, three methods of spectral matching of remote sensing images are presented, namely, pixelwise linear, pixelwise nonlinear, and generalized nonlinear methods. Pixelwise nonlinear and generalized nonlinear methods are implemented as neural networks. The methods are compared using the Landsat-8 and Sentinel-2 images from the IEEE Data Fusion Contest 2021 dataset. According to the experimental results, the generalized nonlinear method of spectral matching of remote sensing images demonstrates the best matching quality.
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