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	<help>
		<element name="DESCRIPTION" text="Principal Components Analysis (PCA) is a technique for analysing between two or more variables. PCA can be used to transform a set of image bands and create a new set of bands which are uncorrelated with each other and ordered according to amount of variance they explain. By taking just those bands that explain a larger amount of variance, redundancy in the data set can be reduced." description="Description" type="0">
		</element>
		<element name="ADDITIONAL_INFO" text="Examples of Principal Components Analysis to land use change detection&#10;&#10;* T. Fung and E. LeDrew. Application of principal component analysis to change detection. Photogrammetric Engineering and Remote Sensing, 53(12):1649&#8211;1658, 1987.  &#10;&#10;* G. F. Byne, P. F. Crapper, and K. K. Mayo. Monitoring land-cover change by principal component analysis of multitemporal landsat data. Remote Sensing of Environment, 10:175&#8211;184, 1980." description="Additional information" type="0">
		</element>
		<element name="EXTENSION_AUTHOR" text="Victor Olaya" description="Extension created by" type="0">
		</element>
		<element name="HELP_AUTHOR" text="Victor Olaya" description="Help file created by" type="0">
		</element>
		<element name="USER_NOTES" text="" description="User_notes" type="0">
		</element>
		<element name="INPUT" text="The set of input bands" description="Input bands" type="3">
		</element>
		<element name="NBANDS" text="Number of bands to take. Must be lower or equal to the number of input bands" description="Numero_bandas" type="3">
		</element>
		<element name="OUTPUT_DESCRIPTION" text="" description="Description" type="2">
		</element>
		<element name="RESULT" text="A new raster layer with the specified number of bands. The first one is the band that explains the largest amount of variance.." description="Principal Components Analysis" type="2">
		</element>
	</help>
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