Cloud detection in Landsat-8 imagery in Google Earth Engine based on a deep neural network
This addresses inefficiencies in cloud detection for remote sensing applications by enabling on-platform processing, though it is incremental as it adapts existing deep learning methods to a specific platform.
The paper tackled cloud detection in Landsat-8 imagery on Google Earth Engine by deploying a deep neural network directly in the platform, eliminating the need for data downloads, and showed it outperformed the widely used Fmask algorithm.
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.