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A Deep Learning Method for Near-Real-Time Cloud and Cloud Shadow Segmentation from Gaofen-1 Images
Computational Intelligence and Neuroscience ( IF 3.120 ) Pub Date : 2020-10-29 , DOI: 10.1155/2020/8811630 Mehdi Khoshboresh-Masouleh 1 , Reza Shah-Hosseini 1
Computational Intelligence and Neuroscience ( IF 3.120 ) Pub Date : 2020-10-29 , DOI: 10.1155/2020/8811630 Mehdi Khoshboresh-Masouleh 1 , Reza Shah-Hosseini 1
Affiliation
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.
更新日期：2020-10-30
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