Khoshboresh-Masouleh Mehdi, Shah-Hosseini Reza
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Comput Intell Neurosci. 2020 Oct 29;2020:8811630. doi: 10.1155/2020/8811630. eCollection 2020.
In this study, an essential application of remote sensing using deep learning functionality is presented. Gaofen-1 satellite mission, developed by the China National Space Administration (CNSA) for the civilian high-definition Earth observation satellite program, provides near-real-time observations for geographical mapping, environment surveying, and climate change monitoring. Cloud and cloud shadow segmentation are a crucial element to enable automatic near-real-time processing of Gaofen-1 images, and therefore, their performances must be accurately validated. In this paper, a robust multiscale segmentation method based on deep learning is proposed to improve the efficiency and effectiveness of cloud and cloud shadow segmentation from Gaofen-1 images. The proposed method first implements feature map based on the spectral-spatial features from residual convolutional layers and the cloud/cloud shadow footprints extraction based on a novel loss function to generate the final footprints. The experimental results using Gaofen-1 images demonstrate the more reasonable accuracy and efficient computational cost achievement of the proposed method compared to the cloud and cloud shadow segmentation performance of two existing state-of-the-art methods.
本研究展示了利用深度学习功能进行遥感的一项重要应用。由中国国家航天局（CNSA）为民用高清地球观测卫星计划开发的高分一号卫星任务，为地理测绘、环境调查和气候变化监测提供近实时观测。云和云影分割是实现高分一号图像自动近实时处理的关键要素，因此，其性能必须得到准确验证。本文提出了一种基于深度学习的鲁棒多尺度分割方法，以提高高分一号图像中云和云影分割的效率和效果。该方法首先基于残差卷积层的光谱 - 空间特征实现特征图，并基于一种新颖的损失函数进行云/云影足迹提取以生成最终足迹。使用高分一号图像的实验结果表明，与两种现有最先进方法的云和云影分割性能相比，该方法具有更合理的精度和更高的计算成本效益。
