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.

摘要

Google Earth Engine（GEE）为基于大面积光学卫星图像的应用程序提供了一个方便的平台。对于此类数据集，检测云通常是必要的先决条件步骤。最近，基于深度学习的云检测方法显示了其在云检测中的潜力，但只能在本地应用，从而导致数据下载时间和存储效率低下。这封信提出了一种基于深度学习（DeepGEE-CD）在GEE中的Landsat-8影像中直接执行云检测的方法。首先在本地训练了深度卷积神经网络（DCNN），然后将训练后的DCNN部署到GEE的JavaScript客户端中。进行了实验，以一套Landsat-8图像验证了该方法的有效性，结果表明DeepGEE-CD的性能优于广泛使用的蒙版（Fmask）算法。提出的DeepGEE-CD方法无需下载即可准确检测Landsat-8影像中的云，这使其成为GEE中Landsat-8影像常规云检测的有前途的方法。