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A Novel Method for Cloud and Cloud Shadow Detection Based on the Maximum and Minimum Values of Sentinel-2 Time Series Images

Remote Sensing, ISSN: 2072-4292, Vol: 16, Issue: 8
2024
  • 2
    Citations
  • 0
    Usage
  • 7
    Captures
  • 2
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    2
    • Citation Indexes
      2
  • Captures
    7
  • Mentions
    2
    • Blog Mentions
      1
      • Blog
        1
    • News Mentions
      1
      • 1

Most Recent News

Research in the Area of Remote Sensing Reported from Ningbo University (A Novel Method for Cloud and Cloud Shadow Detection Based on the Maximum and Minimum Values of Sentinel-2 Time Series Images)

2024 MAY 01 (NewsRx) -- By a News Reporter-Staff News Editor at Tech Daily News -- A new study on remote sensing is now available.

Article Description

Automatic and accurate detection of clouds and cloud shadows is a critical aspect of optical remote sensing image preprocessing. This paper provides a time series maximum and minimum mask method (TSMM) for cloud and cloud shadow detection. Firstly, the Cloud Score+S2_HARMONIZED (CS+S2) is employed as a preliminary mask for clouds and cloud shadows. Secondly, we calculate the ratio of the maximum and sub-maximum values of the blue band in the time series, as well as the ratio of the minimum and sub-minimum values of the near-infrared band in the time series, to eliminate noise from the time series data. Finally, the maximum value of the clear blue band and the minimum value of the near-infrared band after noise removal are employed for cloud and cloud shadow detection, respectively. A national and a global dataset were used to validate the TSMM, and it was quantitatively compared against five other advanced methods or products. When clouds and cloud shadows are detected simultaneously, in the S2ccs dataset, the overall accuracy (OA) reaches 0.93 and the F1 score reaches 0.85. Compared with the most advanced CS+S2, there are increases of 3% and 9%, respectively. In the CloudSEN12 dataset, compared with CS+S2, the producer’s accuracy (PA) and F1 score show increases of 10% and 4%, respectively. Additionally, when applied to Landsat-8 images, TSMM outperforms Fmask, demonstrating its strong generalization capability.

Bibliographic Details

Kewen Liang; Gang Yang; Yangyan Zuo; Jiahui Chen; Weiwei Sun; Binjie Chen; Xiangchao Meng

MDPI AG

Earth and Planetary Sciences

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