TOWARDS ROBUST CLOUD DETECTION IN SATELLITE IMAGES USING U-NETS

被引：10

作者：

Grabowski, Bartosz ^{[1
,2
]}

Ziaja, Maciej ^{[1
]}

Kawulok, Michal ^{[1
,3
]}

Nalepa, Jakub ^{[1
,3
]}

机构：

[1] KP Labs, Konarskiego 18C, PL-44100 Gliwice, Poland

[2] Polish Acad Sci, Inst Theoret & Appl Informat, PL-44100 Gliwice, Poland

[3] Silesian Tech Univ, Akad 16, PL-44100 Gliwice, Poland

来源：

2021 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM IGARSS | 2021年

关键词：

Cloud detection; multispectral images; Landsat-8; imagery; training set selection; U-Net; DETECTION ALGORITHM;

D O I：

10.1109/IGARSS47720.2021.9554170

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

Cloud detection is an important pre-processing step that allows us to significantly reduce the amount of satellite imagery which should undergo further processing. In this paper, we investigate the impact of training set selection on the abilities of fully-convolutional neural networks for this task. Our experiments, performed over a range of Landsat-8 satellite images, show that the performance of deep models can substantially vary for different training samples, especially in the case of challenging scenes, such as those capturing snowy areas.

引用

页码：4099 / 4102

页数：4

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