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Proceedings Paper

Automated cloud cover assessment for Landsat TM images
Author(s): Ben V. Hollingsworth; Liqiang Chen; Stephen E. Reichenbach; Richard R. Irish
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Paper Abstract

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 percent 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 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 forma multi-dimensional histogram analysis of a training subset. Images from a disjoint test set wee then classified. Initial results are significantly more accurate than the existing automated algorithm.

Paper Details

Date Published: 13 November 1996
PDF: 10 pages
Proc. SPIE 2819, Imaging Spectrometry II, (13 November 1996); doi: 10.1117/12.258064
Show Author Affiliations
Ben V. Hollingsworth, Univ. of Nebraska/Lincoln (United States)
Liqiang Chen, Univ. of Nebraska/Lincoln (United States)
Stephen E. Reichenbach, Univ. of Nebraska/Lincoln (United States)
Richard R. Irish, NASA Goddard Space Flight Ctr. (United States)


Published in SPIE Proceedings Vol. 2819:
Imaging Spectrometry II
Michael R. Descour; Jonathan Martin Mooney, Editor(s)

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