Cloud and cloud-shadow detection in SPOT5 HRG imagery with automated morphological feature extraction

Fisher, Adrian (2014) Cloud and cloud-shadow detection in SPOT5 HRG imagery with automated morphological feature extraction. Remote Sensing, 6 1: 776-800. doi:10.3390/rs6010776

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Author Fisher, Adrian
Title Cloud and cloud-shadow detection in SPOT5 HRG imagery with automated morphological feature extraction
Journal name Remote Sensing   Check publisher's open access policy
ISSN 2072-4292
Publication date 2014-01-01
Year available 2014
Sub-type Article (original research)
DOI 10.3390/rs6010776
Open Access Status DOI
Volume 6
Issue 1
Start page 776
End page 800
Total pages 25
Place of publication Basel, Switzerland
Publisher M D P I AG
Language eng
Subject 1900 Earth and Planetary Sciences
Abstract Detecting clouds in satellite imagery is becoming more important with increasing data availability, however many earth observation sensors are not designed for this task. In Satellite pour l'Observation de la Terre 5 (SPOT5) High Resolution Geometrical (HRG) imagery, the reflectance properties of clouds are very similar to common features on the earth's surface, in the four available bands (green, red, near-infrared and shortwave-infrared). The method presented here, called SPOTCASM (SPOT cloud and shadow masking), deals with this problem by using a series of novel image processing steps, and is the first cloud masking method to be developed specifically for SPOT5 HRG imagery. It firstly detects marker pixels using image specific threshold values, and secondly grows segments from these markers using the watershed-from-markers transform. The threshold values are defined as lines in a 2-dimensional histogram of the image surface reflectance values, calculated from two bands. Sun and satellite angles, and the similarity between the area of cloud and shadow objects are used to test their validity. SPOTCASM was tested on an archive of 313 cloudy images from across New South Wales (NSW), Australia, with 95% of images having an overall accuracy greater than 85%. Commission errors due to false clouds (such as highly reflective ground), and false shadows (such as a dark water body) can be high, as can omission errors due to thin cloud that is very similar to the underlying ground surface. These errors can be quickly reduced through manual editing, which is the current method being employed in the operational environment in which SPOTCASM is implemented. The method is being used to mask clouds and shadows from an expanding archive of imagery across NSW, facilitating environmental change detection.
Keyword Cloud
Morphological image processing
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Geography, Planning and Environmental Management Publications
Official 2015 Collection
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Citation counts: TR Web of Science Citation Count  Cited 17 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 18 times in Scopus Article | Citations
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