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

ABSTRACT

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.

Additional information

Funding

This work was supported in part by Hubei Provincial Natural Science Foundation for Innovation Groups (No. 2019CFA019), Strategic Priority Research Program of Chinese Academy of Sciences (No. XDA 2003030201), and the Natural Science Foundation of Anhui Province (No. 1908085QD161).