Automated cloud cover assessment for Landsat TM images

Ben Hollingsworth, Liqiang Chen, Stephen E. Reichenbach, Richard Irish

Research output: Contribution to journalConference articlepeer-review

24 Scopus citations


This paper describes a method for cloud cover assessment using computer-based analysis of multi-band Landsat images. The objective is to accurately determine the percentage of cloud cover in an efficient manner. The "correct" value is determined by an expert's visual assessment. Acceptable error rates are ±10% from the visually-determined coverage. This research improves upon an existing algorithm developed for use by the EROS Data Center several years ago. The existing algorithm uses threshold values in bands 3, 5, and 6 (red, middle infrared, and thermal, respectively) based on the expected frequency response for clouds in each band. While this algorithm is reasonably fast, the accuracy is often unsatisfactory. The dataset used in developing the new method contained 329 subsampled, 7-band Landsat browse images with wide geographic coverage and a variety of cloud types. This dataset, provided by the EROS Data Center, also specifies the visual cloud cover assessment and the cloud cover assessment using the current automated algorithm. Mask images, separating cloud and non-cloud pixels, were developed for a subset of these images. The new approach is statistically based, developed from a multi-dimensional histogram analysis of a training subset. Images from a disjoint test set were then classified. Initial results are significantly more accurate than the existing automated algorithm.

Original languageEnglish (US)
Pages (from-to)170-179
Number of pages10
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - Nov 13 1996
EventImaging Spectrometry II 1996 - Denver, United States
Duration: Aug 4 1996Aug 9 1996


  • Automated cloud cover assessment
  • Environmental sensing
  • Image analysis
  • Multi-spectral analysis
  • Remote sensing

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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