论文标题
基于深神经网络的Google Earth Engine中Landsat-8图像中的云检测
Cloud detection in Landsat-8 imagery in Google Earth Engine based on a deep neural network
论文作者
论文摘要
Google Earth Engine（GEE）为基于大面积光学卫星图像的应用提供了一个方便的平台。使用此类数据集，云的检测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它们的云检测潜力，但只能在本地应用，从而导致数据下载时间和存储问题效率低下。这封信提出了一种基于深度学习（DeepGee-CD）在Gee中直接执行Landsat-8图像中云检测的方法。首先在当地培训了深度神经网络（DNN），然后将训练有素的DNN部署在Gee的JavaScript客户端中。进行了一个实验，以验证一组Landsat-8图像的拟议方法，结果表明，DeepGee-CD的表现优于掩模（FMASK）算法的广泛使用的函数。提出的DeepGee-CD方法可以在不下载它的情况下准确地检测到Landsat-8图像中的云，从而使其成为Gee中常规云检测Landsat-8图像的有前途的方法。
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 neural network (DNN) was first trained locally, and then the trained DNN 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.
