000	03094nam  2200325za 4500
001	9.837232
003	CaOODSP
005	20221107151105
007	cr |||||||||||
008	170525s2016    oncabd  ob   f000 0 eng d
040	|aCaOODSP|beng
043	|an-cn---|an-cn-ab
086	1 |aM103-3/23-2016E-PDF
100	1 |aPouliot, Darren,|d1975-
245	10|aInfluence of sample distribution and prior probability adjustment on land cover classification |h[electronic resource] / |cD. Pouliot, R. Latifovic, W. Parkinson.
260	|a[Ottawa] : |bNatural Resources Canada, |c2016.
300	|a[13] p. : |bcol. charts, col. ill., col. maps.
490	1 |aOpen file ; |v23
504	|aIncludes bibliographical references.
520	3 |a"Machine 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 adjustment 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”--Abstract, p. [3].
692	07|2gccst|aGeophysics
692	07|2gccst|aRemote sensing
692	07|2gccst|aSatellite imagery
692	07|2gccst|aMethodology
700	1 |aLatifovic, Rasim, |d1959-
700	1 |aParkinson, William,|d1986-
710	1 |aCanada. |bNatural Resources Canada.
710	2 |aGeomatics Canada.
830	#0|aOpen file (Geomatics Canada)|v23.|w(CaOODSP)9.821474
856	40|qPDF|s1.04 MB|uhttps://publications.gc.ca/collections/collection_2017/rncan-nrcan/M103-3/M103-3-23-2016-eng.pdf