Using Convolutional Neural Networks for Cloud Detection on VENμS Images over Multiple Land-Cover Types

Ondřej Pešek, Michal Segal-Rozenhaimer, Arnon Karnieli

Research output: Contribution to journalArticlepeer-review

Abstract

In most parts of the electromagnetic spectrum, solar radiation cannot penetrate clouds. Therefore, cloud detection and masking are essential in image preprocessing for observing the Earth and analyzing its properties. Because clouds vary in size, shape, and structure, an accurate algorithm is required for removing them from the area of interest. This task is usually more challenging over bright surfaces such as exposed sunny deserts or snow than over water bodies or vegetated surfaces. The overarching goal of the current study is to explore and compare the performance of three Convolutional Neural Network architectures (U-Net, SegNet, and DeepLab) for detecting clouds in the VEN (Formula presented.) S satellite images. To fulfil this goal, three VEN (Formula presented.) S tiles in Israel were selected. The tiles represent different land-use and cover categories, including vegetated, urban, agricultural, and arid areas, as well as water bodies, with a special focus on bright desert surfaces. Additionally, the study examines the effect of various channel inputs, exploring possibilities of broader usage of these architectures for different data sources. It was found that among the tested architectures, U-Net performs the best in most settings. Its results on a simple RGB-based dataset indicate its potential value for any satellite system screening, at least in the visible spectrum. It is concluded that all of the tested architectures outperform the current VEN (Formula presented.) S cloud-masking algorithm by lowering the false positive detection ratio by tens of percents, and should be considered an alternative by any user dealing with cloud-corrupted scenes.

Original languageEnglish
Article number5210
JournalRemote Sensing
Volume14
Issue number20
DOIs
StatePublished - 1 Oct 2022

Keywords

  • CNN
  • artificial neural network
  • deep learning
  • remote sensing
  • semantic segmentation

ASJC Scopus subject areas

  • Earth and Planetary Sciences (all)

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