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TitleInfluence of sample distribution and prior probability adjustment on land cover classification
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LicencePlease note the adoption of the Open Government Licence - Canada supersedes any previous licences.
AuthorPouliot, D; Latifovic, R; Parkinson, W
SourceGeomatics Canada, Open File 23, 2016, 13 pages, https://doi.org/10.4095/297517 Open Access logo Open Access
Year2016
PublisherNatural Resources Canada
Documentopen file
Lang.English
Mediaon-line; digital
File formatpdf
ProvinceAlberta
NTS74D; 74E; 84A; 84H
AreaFort McMurray; Athabasca River
Lat/Long WENS-114.0000 -110.0000 58.0000 56.0000
Subjectsgeophysics; mathematical and computational geology; modelling; analytical methods; LANDSAT; landform classification
Illustrationssatellite images; plots
ProgramRemote Sensing Science, Methodology
Released2016 01 28
AbstractMachine learning algorithms are widely used for remote sensing land surface characterization. Successful implementation requires a representative training sample for the domain it will applied in (i.e. area of interest or validation domain). However, accessibility and cost strongly limit the acquisition of suitable training samples for large regional applications. Further, it is often desirable to use previously developed datasets where significant resources have been invested, such as data developed from extensive field survey or high resolution remotely sensed imagery. These data often only partially represent the domain of interest and can lead to various forms of sample bias (land cover distribution or class properties). Classifier spatial extension is an extreme case, where a sample is trained from one region (i.e. sample domain) and applied in another (i.e. application domain). This approach is desirable from a cost perspective, but achieving acceptable accuracy is often difficult. In this research we investigate two approaches to account for possible differences between the sample and application domain land cover distributions. The first is an iterative resampling approach to predict the application distribution and adjust the sample distribution to match. The second is the use of prior probabilities to adjust class memberships. Results reinforce the importance of the land cover distribution on accuracy for algorithms that are designed to minimize the classification error with training data. Of the adjustments methods tested resampling was superior if the application domain distribution was well known. However, if it is not then the use of prior probabilities performed similarly overall. A generic model was developed to predict if resampling or prior adjustment should be applied to enhance accuracy.
Summary(Plain Language Summary, not published)
Open file is a technical document examining the role of sampling and correction mechanisms to sampling for enhancement of land cover mapping from remote sensing data using machine learning algorithms that seek to minimize the training data error.
GEOSCAN ID297517

 
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