MixChannel: Advanced Augmentation for Multispectral Satellite Images

编号 030029401

推送时间 20210607

研究领域 森林经理 

年份 2021 

类型 期刊 

语种 英语

标题 MixChannel: Advanced Augmentation for Multispectral Satellite Images

来源期刊 REMOTE SENSING

期 第294期

发表时间 20210603

关键词 image augmentation;  remote sensing;  multispectral imagery;  forest inventory; 

摘要 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.

服务人员 付贺龙

服务院士 唐守正

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