Cloud detection in Landsat-8 imagery in Google Earth Engine based on a deep convolutional neural network

Yin, Zhixiang; Ling, Feng; Foody, Giles M.; Li, Xinyan; Du, Yun

Abstract

© 2020 Informa UK Limited, trading as Taylor & Francis Group. Google Earth Engine (GEE) provides a convenient platform for applications based on optical satellite imagery of large areas. With such data sets, the detection of cloud is often a necessary prerequisite step. Recently, deep learning-based cloud detection methods have shown their potential for cloud detection but they can only be applied locally, leading to inefficient data downloading time and storage problems. This letter proposes a method to directly perform cloud detection in Landsat-8 imagery in GEE based on deep learning (DeepGEE-CD). A deep convolutional neural network (DCNN) was first trained locally, and then the trained DCNN was deployed in the JavaScript client of GEE. An experiment was undertaken to validate the proposed method with a set of Landsat-8 images and the results show that DeepGEE-CD outperformed the widely used function of mask (Fmask) algorithm. The proposed DeepGEE-CD approach can accurately detect cloud in Landsat-8 imagery without downloading it, making it a promising method for routine cloud detection of Landsat-8 imagery in GEE.

Citation

Yin, Z., Ling, F., Foody, G. M., Li, X., & Du, Y. (2020). Cloud detection in Landsat-8 imagery in Google Earth Engine based on a deep convolutional neural network. Remote Sensing Letters, 11(12), 1181-1190. https://doi.org/10.1080/2150704X.2020.1833096

• Journal Article Type: Article
• Acceptance Date: Sep 30, 2020
• Online Publication Date: Nov 19, 2020
• Publication Date: Dec 1, 2020
• Deposit Date: Dec 17, 2020
• Publicly Available Date: Jan 6, 2021
• Journal: Remote Sensing Letters
• Print ISSN: 2150-704X
• Electronic ISSN: 2150-7058
• Publisher: Taylor and Francis
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Issue: 12
• Pages: 1181-1190
• DOI: https://doi.org/10.1080/2150704X.2020.1833096
• Keywords: Electrical and Electronic Engineering; Earth and Planetary Sciences (miscellaneous)
• Public URL: https://nottingham-repository.worktribe.com/output/5073556
• Publisher URL: https://www.tandfonline.com/doi/full/10.1080/2150704X.2020.1833096
• Additional Information: Peer Review Statement: The publishing and review policy for this title is described in its Aims & Scope.; Aim & Scope: http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=trsl20; Received: 2020-06-04; Accepted: 2020-09-30; Published: 2020-11-19