A deep learning method for Landsat image cloud detection without manually labeled data

doi:10.6046/gtzyyg.2020090

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Abstract

Cloud and cloud shadow detection is an important part in the production of Landsat images. In recent years, deep learning has greatly improved the accuracy of cloud detection in Landsat images. However, deep convolutional neural network model training relies on a large scale of labeled images, and it is necessary to manually label each pixel as clearness, cloud or cloud shadow. Manually labeling is rather costly and time-consuming, which is not conducive to train practical models. Inspired by weakly supervised learning, this paper proposes a new deep learning method for cloud and cloud shadow detection. Firstly, conventional cloud detection algorithm CFMask is used to detect cloud and cloud shadow in Landsat images. Then, the results are used to replace the manually labeled images to train the deep convolutional neural network model for cloud detection. Finally, the model is used to detect the cloud and its shadow in new images. Experimental results show that the overall accuracy of the proposed method is 85.55%, which is better than that of CFMask and indicates that it is feasible to train the deep network model to detect cloud and cloud shadow without manually labeled data.

Keywords cloud and cloud shadow detection Landsat8 image convolutional neural network semantic segmentation

ZTFLH:

TP751.1

Corresponding Authors: CHAI Dengfeng E-mail: rs_qyf@zju.edu.cn;chaidf@zju.edu.cn

Issue Date: 18 March 2021

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	Yifan QIU
	Dengfeng CHAI

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Yifan QIU,Dengfeng CHAI. A deep learning method for Landsat image cloud detection without manually labeled data[J]. Remote Sensing for Land & Resources, 2021, 33(1): 102-107.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020090 OR https://www.gtzyyg.com/EN/Y2021/V33/I1/102

Tab.1 Landsat8 Collection 1 Level 1 quality band bit designations

Tab.2 The number of subscenes and images in sample set

Fig.1 Technical flowchart of experimental scheme

Fig.2 Network structure based on SegNet

Tab.3 Cloud and cloud shadow detection results

Tab.4 Evaluation of detection results(%)

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