Convolutional Neural Networks for Multispectral Image Cloud Masking
This work addresses the problem of accurate cloud masking for remote sensing image analysis, which is crucial for downstream applications, primarily benefiting remote sensing practitioners and researchers.
This paper explores the application of various Convolutional Neural Network (CNN) architectures for cloud masking in Proba-V multispectral images. The study compares CNN performance against traditional machine learning methods that rely on feature extraction and supervised classification, indicating CNNs as a promising alternative for this task.
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.