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Article

MixChannel: Advanced Augmentation for Multispectral Satellite Images

Skolkovo Institute of Science and Technology, 143026 Moscow, Russia
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Academic Editors: Petri Pellikka, Alireza Hamedianfar and Helmi Shafri
Remote Sens. 2021, 13(11), 2181; https://doi.org/10.3390/rs13112181
Received: 1 May 2021 / Revised: 28 May 2021 / Accepted: 31 May 2021 / Published: 3 June 2021
(This article belongs to the Special Issue Satellite Image Processing and Applications)
Usage of multispectral satellite imaging data opens vast possibilities for monitoring and quantitatively assessing properties or objects of interest on a global scale. Machine learning and computer vision (CV) approaches show themselves as promising tools for automatizing satellite image analysis. However, there are limitations in using CV for satellite data. Mainly, the crucial one is the amount of data available for model training. This paper presents a novel image augmentation approach called MixChannel that helps to address this limitation and improve the accuracy of solving segmentation and classification tasks with multispectral satellite images. The core idea is to utilize the fact that there is usually more than one image for each location in remote sensing tasks, and this extra data can be mixed to achieve the more robust performance of the trained models. The proposed approach substitutes some channels of the original training image with channels from other images of the exact location to mix auxiliary data. This augmentation technique preserves the spatial features of the original image and adds natural color variability with some probability. We also show an efficient algorithm to tune channel substitution probabilities. We report that the MixChannel image augmentation method provides a noticeable increase in performance of all the considered models in the studied forest types classification problem. View Full-Text
Keywords: image augmentation; remote sensing; multispectral imagery; forest inventory image augmentation; remote sensing; multispectral imagery; forest inventory
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MDPI and ACS Style

Illarionova , S.; Nesteruk , S.; Shadrin, D.; Ignatiev , V.; Pukalchik , M.; Oseledets, I. MixChannel: Advanced Augmentation for Multispectral Satellite Images. Remote Sens. 2021, 13, 2181. https://doi.org/10.3390/rs13112181

AMA Style

Illarionova  S, Nesteruk  S, Shadrin D, Ignatiev  V, Pukalchik  M, Oseledets I. MixChannel: Advanced Augmentation for Multispectral Satellite Images. Remote Sensing. 2021; 13(11):2181. https://doi.org/10.3390/rs13112181

Chicago/Turabian Style

Illarionova , Svetlana, Sergey Nesteruk , Dmitrii Shadrin, Vladimir Ignatiev , Maria Pukalchik , and Ivan Oseledets. 2021. "MixChannel: Advanced Augmentation for Multispectral Satellite Images" Remote Sensing 13, no. 11: 2181. https://doi.org/10.3390/rs13112181

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