Convolutional neural networks for multispectral image cloud masking

Convolutional neural networks for multispectral image cloud masking
复制标题

DOI：
10.1109/igarss.2017.8127438
复制
发表时间：
2017-07
期刊：
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
影响因子：
--
通讯作者：
Gonzalo Mateo-García;L. Gómez-Chova;Gustau Camps-Valls
Gonzalo Mateo-García;L. Gómez-Chova;Gustau Camps-Valls
中科院分区：
其他
文献类型：
--
作者：
Gonzalo Mateo-García;L. Gómez-Chova;Gustau Camps-Valls

文献摘要

被引文献

相似文献

卷积神经网络（CNN）已被证明是许多图像分类任务的最先进的方法，它们的使用正在迅速增加遥感问题。其主要优势之一是，当有足够的数据可用时，CNN可以执行端到端学习，而无需自定义特征提取方法。在这项工作中，我们研究了使用不同的CNN架构对Proba-V多光谱图像进行云掩蔽。我们比较这些方法与更经典的机器学习方法的基础上，特征提取加监督分类。实验结果表明，CNN是解决云掩蔽问题的一种有前途的替代方案。
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.