“利用深度学习从卫星图像中检测和去除云影响区域”

IF 0.4 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Lawrence Egharevba, Sanjoy Kumar, N. Rishe, Hadi Amini, Malek Adjouadi
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引用次数: 0

摘要

深度学习正在成为一种非常流行的生成和重建图像的工具。研究表明,深度学习算法可以对各种类型的图像执行尖端的恢复任务。这些算法的性能可以通过使用大样本量的数据训练深度卷积神经网络(DCNNs)来实现。当数据集中只有少量图像时,高分辨率卫星图像的处理变得困难。本文提出了一种基于深度卷积神经网络(Deep Convolutional Neural Networks, DCNNs)固有特性的方法,用于在不经过任何预先训练的情况下从遥感图像中检测和去除云。我们的结果表明,与经过训练的算法相比,我们使用的算法表现良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
"Detecting and Removing Clouds Affected Regions from Satellite Images Using Deep Learning"
Deep Learning is becoming a very popular tool for generating and reconstructing images. Research has shown that deep learning algorithms can perform cutting-edge restoration tasks for various types of images. The performance of these algorithms can be achieved by training Deep Convolutional Neural Networks (DCNNs) with data from a large sample size. The processing of high-resolution satellite imagery becomes difficult when there are only a few images in a dataset. An approach based on the intrinsic properties of Deep Convolutional Neural Networks (DCNNs) is presented in this paper for the detection and removal of clouds from remote sensing images without any prior training. Our results demonstrated that the algorithm we used performed well when compared to trained algorithms.
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来源期刊
IPSI BgD Transactions on Internet Research
IPSI BgD Transactions on Internet Research COMPUTER SCIENCE, INFORMATION SYSTEMS-
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