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Distinguishing Cloud and Snow in Satellite Images via Deep Convolutional Network by Yongjie Zhan; Jian Wang; Jianping Shi; Guangliang Cheng; Lele Yao; Weidong Sun is a Engineering article available to read on EtoBox.

What is Distinguishing Cloud and Snow in Satellite Images via Deep Convolutional Network about?

Cloud and snow detection has significant remote sensing applications, while they share similar low-level features due to their consistent color distributions and similar local texture patterns. Thus, accurately distinguishing cloud from snow in pixel level from satellite images is always a challenging task with traditional approaches. To solve this shortcoming, in this letter, we proposed a deep learning system to classify cloud and snow with fully convolutional neural networks in pixel level. Specifically, a specially designed fully convolutional network was introduced to learn deep patterns for cloud and snow detection from the multispectrum satellite images. Then, a multiscale prediction strategy was introduced to integrate the low-level spatial information and high-level semantic information simultaneously. Finally, a new and challenging cloud and snow data set was labeled manually to train and further evaluate the proposed method. Extensive experiments demonstrate that the proposed deep model outperforms the state-of-the-art methods greatly both in quantitative and qualitative performances.

Who reads Distinguishing Cloud and Snow in Satellite Images via Deep Convolutional Network?

Author

Yongjie Zhan; Jian Wang; Jianping Shi; Guangliang Cheng; Lele Yao; Weidong Sun

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2017

Language

EN

Field

Engineering (Physical Sciences)