# Convolutional Neural Networks for Multispectral Image Cloud Masking

arXiv: https://arxiv.org/abs/2012.05325 · HTML: https://mlsanity.com/paper/2012.05325/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [A lightweight deep learning based cloud detection method for Sentinel-2A imagery fusing multi-scale spectral and spatial features](https://mlsanity.com/paper/2105.00967/) (2021)
- [CloudMamba: An Uncertainty-Guided Dual-Scale Mamba Network for Cloud Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2604.06844/) (2026)

## Abstract (arXiv)

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.