论文标题
用于多光谱图像云掩蔽的卷积神经网络
Convolutional Neural Networks for Multispectral Image Cloud Masking
论文作者
论文摘要
事实证明，卷积神经网络（CNN）是许多图像分类任务的最先进方法的状态，并且它们在遥感问题中的使用正在迅速增加。他们的主要优势之一是，当有足够的数据可用时，CNN执行端到端的学习，而无需自定义特征提取方法。在这项工作中，我们研究了不同CNN体系结构的使用来掩盖Proba-V多光谱图像。我们将这些方法与基于功能提取以及监督分类的更古典的机器学习方法进行了比较。实验结果表明，CNN是解决云掩蔽问题的有前途的替代方法。
Convolutional neural networks (CNN) have proven to be state of the art methods for many image classification tasks and their use is rapidly increasing in remote sensing problems. One of their major strengths is that, when enough data is available, CNN perform an end-to-end learning without the need of custom feature extraction methods. In this work, we study the use of different CNN architectures for cloud masking of Proba-V multispectral images. We compare such methods with the more classical machine learning approach based on feature extraction plus supervised classification. Experimental results suggest that CNN are a promising alternative for solving cloud masking problems.
