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http://dx.doi.org/10.7780/kjrs.2022.38.5.1.6

A Study on the Cloud Detection Technique of Heterogeneous Sensors Using Modified DeepLabV3+  

Kim, Mi-Jeong (Defense Artificial Intelligence Technology Center, Agency for Defense Development)
Ko, Yun-Ho (Department of Mechatronics Engineering, Chungnam National University)
Publication Information
Korean Journal of Remote Sensing / v.38, no.5_1, 2022 , pp. 511-521 More about this Journal
Abstract
Cloud detection and removal from satellite images is an essential process for topographic observation and analysis. Threshold-based cloud detection techniques show stable performance because they detect using the physical characteristics of clouds, but they have the disadvantage of requiring all channels' images and long computational time. Cloud detection techniques using deep learning, which have been studied recently, show short computational time and excellent performance even using only four or less channel (RGB, NIR) images. In this paper, we confirm the performance dependence of the deep learning network according to the heterogeneous learning dataset with different resolutions. The DeepLabV3+ network was improved so that channel features of cloud detection were extracted and learned with two published heterogeneous datasets and mixed data respectively. As a result of the experiment, clouds' Jaccard index was low in a network that learned with different kind of images from test images. However, clouds' Jaccard index was high in a network learned with mixed data that added some of the same kind of test data. Clouds are not structured in a shape, so reflecting channel features in learning is more effective in cloud detection than spatial features. It is necessary to learn channel features of each satellite sensors for cloud detection. Therefore, cloud detection of heterogeneous sensors with different resolutions is very dependent on the learning dataset.
Keywords
Cloud detection; DeepLabV3+;
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Times Cited By KSCI : 1  (Citation Analysis)
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