ILMSImage Classification - Revision history

DanielS at 00:52, 13 February 2012

2012-02-13T00:52:28Z

← Older revision		Revision as of 00:52, 13 February 2012
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	===Importing Reference Areas===		===Importing Reference Areas===

−	In order to make the defined reference dataset suitable for thematic classification, ~~the~~ it has to be imported into the ILMSImage project. This means, first of all, that the data have to be loaded in the current QuantumGIS project - no matter how it has been generated. In the list box ''Reference data layer'' all loaded vector levels are listed which have the above-described structure and are therefore considered importable by ILMSImage. After the layer required has been selected, clicking on ''Conduct classification'' starts the actual import of reference data. This transforms the vector dataset into raster format and added to the attribute list of the cells. This is supposed to happen usually very fast. The complete result can be represented in the current map view by clicking on ''Visualize results''. The result for the above-described example of a reference dataset can be like this (subject to the underlying cell geometries):	+	In order to make the defined reference dataset suitable for thematic classification, it has to be imported into the ILMSImage project. This means, first of all, that the data have to be loaded in the current QuantumGIS project - no matter how it has been generated. In the list box ''Reference data layer'' all loaded vector levels are listed which have the above-described structure and are therefore considered importable by ILMSImage. After the layer required has been selected, clicking on ''Conduct classification'' starts the actual import of reference data. This transforms the vector dataset into raster format and added to the attribute list of the cells. This is supposed to happen usually very fast. The complete result can be represented in the current map view by clicking on ''Visualize results''. The result for the above-described example of a reference dataset can be like this (subject to the underlying cell geometries):

	[[File:ilms_img_qgis_work_41.png\|800px\|Importierter und visualisierter Referenzdatensatz]]		[[File:ilms_img_qgis_work_41.png\|800px\|Importierter und visualisierter Referenzdatensatz]]

DanielS at 00:22, 13 February 2012

2012-02-13T00:22:06Z

← Older revision		Revision as of 00:22, 13 February 2012
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	<ol>		<ol>
−	<li>First the support points of the polygon are generated by left-clicking at the corresponding coordinates. It is not necessary to be very precise since ILMSImage is able to separate potential peripheries from central areas of a reference area by statistic criteria. Instead of being accurate it often makes more ~~sence~~ to increase the sample by generating various reference areas for a thematic class. <br/><br/>	+	<li>First the support points of the polygon are generated by left-clicking at the corresponding coordinates. It is not necessary to be very precise since ILMSImage is able to separate potential peripheries from central areas of a reference area by statistic criteria. Instead of being accurate it often makes more sense to increase the sample by generating various reference areas for a thematic class. <br/><br/>
	[[File:ilms_img_qgis_work_35.png\|400px\|Digitalisieren eines Referenzpolygons]]<br/><br/></li>		[[File:ilms_img_qgis_work_35.png\|400px\|Digitalisieren eines Referenzpolygons]]<br/><br/></li>
	<li>When all support points are set, a right-click finishes the digitization of the polygon. The following input mask the class ID and the corresponding description are indicated. It should be noted again that the numbering of classes starts from one and has to be consecutive.<br/><br/>		<li>When all support points are set, a right-click finishes the digitization of the polygon. The following input mask the class ID and the corresponding description are indicated. It should be noted again that the numbering of classes starts from one and has to be consecutive.<br/><br/>

DanielS: /* Unsupervised Classification into Cell Type Classes */

2012-02-07T14:41:52Z

Unsupervised Classification into Cell Type Classes

← Older revision		Revision as of 14:41, 7 February 2012
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	ILMSImage implements two methods of cluster analysis:		ILMSImage implements two methods of cluster analysis:

−	# A '''k-Means algorithm''' which represents a frequently used method for finding structures in large amounts of data. The number of clusters of the clusters which should be found is determined beforehand. After a random initialization every cell is assigned to the cluster the central feature vector of which is most similar to that of the cell. If this initial assignment is completed, the cluster centers are recalculated and every cell is compared to existing clusters again. These steps are repeated until none of the assignments change again - ~~the~~ the cluster analysis is completed.	+	# A '''k-Means algorithm''' which represents a frequently used method for finding structures in large amounts of data. The number of clusters of the clusters which should be found is determined beforehand. After a random initialization every cell is assigned to the cluster the central feature vector of which is most similar to that of the cell. If this initial assignment is completed, the cluster centers are recalculated and every cell is compared to existing clusters again. These steps are repeated until none of the assignments change again - the cluster analysis is completed.
	# Another method for partitional clustering is the algorithm of '''[http://en.wikipedia.org/wiki/Self-organizing_map\| self-organizing maps]''' which was originally developed by Teuvu Kohonen. It is an artificial neuronal network which is able to project a multi-dimensional feature space functioning as a data source on a two-dimensional range of values. This characteristic explains the relation to the ''map''. The detailed functioning of this algorithm is described at another point.		# Another method for partitional clustering is the algorithm of '''[http://en.wikipedia.org/wiki/Self-organizing_map\| self-organizing maps]''' which was originally developed by Teuvu Kohonen. It is an artificial neuronal network which is able to project a multi-dimensional feature space functioning as a data source on a two-dimensional range of values. This characteristic explains the relation to the ''map''. The detailed functioning of this algorithm is described at another point.

DanielS: /* Introduction */

2012-02-07T14:10:43Z

Introduction

← Older revision		Revision as of 14:10, 7 February 2012
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	==Introduction==		==Introduction==

−	''ILMSImage Classification'' is part of the [[Tutorial_ILMSImage#ILMSImage_Plug-in_for_QuantumGIS\|ILMSImage plug-in for QuantumGIS]] and in this context it carries out a two-step thematic analysis, comprising unsupervised and supervised classification. u	+	''ILMSImage Classification'' is part of the [[Tutorial_ILMSImage#ILMSImage_Plug-in_for_QuantumGIS\|ILMSImage plug-in for QuantumGIS]] and in this context it carries out a two-step thematic analysis, comprising unsupervised and supervised classification.

	Like various ILMSImage panels it consists of two components, the ''[[ILMSImage_Cell_Creation#Part_1:_Information\|ILMSImage Project Information]]'' in the upper section and the actual tools in the lower section.		Like various ILMSImage panels it consists of two components, the ''[[ILMSImage_Cell_Creation#Part_1:_Information\|ILMSImage Project Information]]'' in the upper section and the actual tools in the lower section.

DanielS at 12:17, 13 January 2012

2012-01-13T12:17:17Z

← Older revision		Revision as of 12:17, 13 January 2012
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	[[de:ILMSImage_Classification]]		[[de:ILMSImage_Classification]]
		+	[[pt:ILMSImage_Classification]]
	=[[File: ilms_img_classification_icon.png\|50px\|<span title=""></span>]] ILMSImage Classification=		=[[File: ilms_img_classification_icon.png\|50px\|<span title=""></span>]] ILMSImage Classification=

Chr at 08:13, 5 December 2011

2011-12-05T08:13:05Z

Chr: /* Unsupervised Classification into Cell Type Classes */

2011-11-30T17:07:10Z

Unsupervised Classification into Cell Type Classes

← Older revision		Revision as of 17:07, 30 November 2011
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	===Unsupervised Classification into Cell Type Classes===		===Unsupervised Classification into Cell Type Classes===

−	The unsupervised classification refers to generated geometries during the [[ILMSImage Cell Creation ~~english~~\|cell creation]] and the [[ILMSImage Attribute Calculation ~~english~~\|calculated attributes]] for the geometries to derive cell type classes from both components. They are group of cells which - no matter which geographical location in the image or location to one another - have similar features. Simply put, in this task e.g. elongated and dark cells are separated from those which are rather round and bright - only that the corresponding decision in reality is not limited to the named features but is based on all features generated during attribute calculation. The basic concept of this task therefore corresponds to the partitional cluster analysis ([http://en.wikipedia.org/wiki/Cluster_analysis#Partitional_clustering\| Partitional clustering]).	+	The unsupervised classification refers to generated geometries during the [[ILMSImage Cell Creation\|cell creation]] and the [[ILMSImage Attribute Calculation\|calculated attributes]] for the geometries to derive cell type classes from both components. They are group of cells which - no matter which geographical location in the image or location to one another - have similar features. Simply put, in this task e.g. elongated and dark cells are separated from those which are rather round and bright - only that the corresponding decision in reality is not limited to the named features but is based on all features generated during attribute calculation. The basic concept of this task therefore corresponds to the partitional cluster analysis ([http://en.wikipedia.org/wiki/Cluster_analysis#Partitional_clustering\| Partitional clustering]).

	ILMSImage implements two methods of cluster analysis:		ILMSImage implements two methods of cluster analysis:

Chr: /* Introduction */

2011-11-30T17:06:43Z

Introduction

← Older revision		Revision as of 17:06, 30 November 2011
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	''ILMSImage Classification'' is part of the [[Tutorial_ILMSImage#ILMSImage_Plug-in_for_QuantumGIS\|ILMSImage plug-in for QuantumGIS]] and in this context it carries out a two-step thematic analysis, comprising unsupervised and supervised classification. u		''ILMSImage Classification'' is part of the [[Tutorial_ILMSImage#ILMSImage_Plug-in_for_QuantumGIS\|ILMSImage plug-in for QuantumGIS]] and in this context it carries out a two-step thematic analysis, comprising unsupervised and supervised classification. u

−	Like various ILMSImage panels it consists of two components, the ''[[~~ILMSImage_Cell_Creation_english~~#Part_1:_Information\|ILMSImage Project Information]]'' in the upper section and the actual tools in the lower section.	+	Like various ILMSImage panels it consists of two components, the ''[[ILMSImage_Cell_Creation#Part_1:_Information\|ILMSImage Project Information]]'' in the upper section and the actual tools in the lower section.

	[[Bild:ilms_img_qgis_work_16.png\|Das ILMSImage-Panel zur thematischen Klassifikation]]		[[Bild:ilms_img_qgis_work_16.png\|Das ILMSImage-Panel zur thematischen Klassifikation]]

Chr at 15:58, 28 November 2011

2011-11-28T15:58:17Z

← Older revision		Revision as of 15:58, 28 November 2011
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		+	[[de:ILMSImage_Classification]]
	=[[Bild: ilms_img_classification_icon.png\|50px\|<span title=""></span>]] ILMSImage Classification=		=[[Bild: ilms_img_classification_icon.png\|50px\|<span title=""></span>]] ILMSImage Classification=

Chr: hat „ILMSImage Classification english“ nach „ILMSImage Classification“ verschoben

2011-11-28T15:32:40Z

hat „ILMSImage Classification english“ nach „ILMSImage Classification“ verschoben

← Older revision

Revision as of 15:32, 28 November 2011

← Older revision		Revision as of 17:06, 30 November 2011
Line 6:		Line 6:
	''ILMSImage Classification'' is part of the [[Tutorial_ILMSImage#ILMSImage_Plug-in_for_QuantumGIS\|ILMSImage plug-in for QuantumGIS]] and in this context it carries out a two-step thematic analysis, comprising unsupervised and supervised classification. u		''ILMSImage Classification'' is part of the [[Tutorial_ILMSImage#ILMSImage_Plug-in_for_QuantumGIS\|ILMSImage plug-in for QuantumGIS]] and in this context it carries out a two-step thematic analysis, comprising unsupervised and supervised classification. u

−	Like various ILMSImage panels it consists of two components, the ''[[~~ILMSImage_Cell_Creation_english~~#Part_1:_Information\|ILMSImage Project Information]]'' in the upper section and the actual tools in the lower section.	+	Like various ILMSImage panels it consists of two components, the ''[[ILMSImage_Cell_Creation#Part_1:_Information\|ILMSImage Project Information]]'' in the upper section and the actual tools in the lower section.

	[[Bild:ilms_img_qgis_work_16.png\|Das ILMSImage-Panel zur thematischen Klassifikation]]		[[Bild:ilms_img_qgis_work_16.png\|Das ILMSImage-Panel zur thematischen Klassifikation]]

← Older revision		Revision as of 08:13, 5 December 2011
Line 1:		Line 1:
	[[de:ILMSImage_Classification]]		[[de:ILMSImage_Classification]]
−	=[[~~Bild~~: ilms_img_classification_icon.png\|50px\|<span title=""></span>]] ILMSImage Classification=	+	=[[File: ilms_img_classification_icon.png\|50px\|<span title=""></span>]] ILMSImage Classification=

	==Introduction==		==Introduction==
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	Like various ILMSImage panels it consists of two components, the ''[[ILMSImage_Cell_Creation#Part_1:_Information\|ILMSImage Project Information]]'' in the upper section and the actual tools in the lower section.		Like various ILMSImage panels it consists of two components, the ''[[ILMSImage_Cell_Creation#Part_1:_Information\|ILMSImage Project Information]]'' in the upper section and the actual tools in the lower section.

−	[[~~Bild~~:ilms_img_qgis_work_16.png\|Das ILMSImage-Panel zur thematischen Klassifikation]]	+	[[File:ilms_img_qgis_work_16.png\|Das ILMSImage-Panel zur thematischen Klassifikation]]

	==Thematic Classification==		==Thematic Classification==
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	The continuing processing is indicated by a corresponding window. According to the number of selected attributes and to the selected sensitivity the duration of calculation time can vary greatly. After successful completion of the process the user is informed about the number of generated cell type classes:		The continuing processing is indicated by a corresponding window. According to the number of selected attributes and to the selected sensitivity the duration of calculation time can vary greatly. After successful completion of the process the user is informed about the number of generated cell type classes:

−	[[~~Bild~~:ilms_img_qgis_work_30.png\|Benachrichtigungsfenster nach Abschluss der unüberwachten Klassifikation]]	+	[[File:ilms_img_qgis_work_30.png\|Benachrichtigungsfenster nach Abschluss der unüberwachten Klassifikation]]

	If the check box ''Unsupervised classification'' stays activated, one click on ''Visualize results'' can load the result of the unsupervised classification into the current map view. The colors used for the visualization are random colors, this is they do not possess any semantic significance in relation to the thematic class of a cell. They only illustrate their common belonging to one cell type class. In the key the raster data layer appears in the group ''ILMSImage Exports'' specified as <code><project name>_sample</code>.		If the check box ''Unsupervised classification'' stays activated, one click on ''Visualize results'' can load the result of the unsupervised classification into the current map view. The colors used for the visualization are random colors, this is they do not possess any semantic significance in relation to the thematic class of a cell. They only illustrate their common belonging to one cell type class. In the key the raster data layer appears in the group ''ILMSImage Exports'' specified as <code><project name>_sample</code>.

−	[[~~Bild~~:ilms_img_qgis_work_31.png\|800px\|Im aktuellen QuantumGIS-Projekt visualisiertes Ergebnis der unüberwachten Klassifkation]]	+	[[File:ilms_img_qgis_work_31.png\|800px\|Im aktuellen QuantumGIS-Projekt visualisiertes Ergebnis der unüberwachten Klassifkation]]

	It should be noted that only one result can exist in this task which can be used for further thematic classification. If such a result has not been visualized after its generation as described above, the raster layer shown in the map view most likely does not correspond to this data basis.		It should be noted that only one result can exist in this task which can be used for further thematic classification. If such a result has not been visualized after its generation as described above, the raster layer shown in the map view most likely does not correspond to this data basis.
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	In order to facilitate the generation of a new reference data set for the user, the QuantumGIS plug-in makes a corresponding shapefile available which has the right structure and can be complemented with area borders and their attributions. The function can be activated by clicking on ''Create reference layer template'', after the entire section ''Reference data import'' has been activated by using the corresponding check box. The following dialog requests the name of the file which is supposed to be generated (the default setting is <code><project name>_ref</code>) and asks the user to determine the estimated number of thematic classes. This indication is not final, subsequently classes can be added or removed - the only function is to make the determined number of classes (with different visual representation in terms of color) available for the definition of reference areas.		In order to facilitate the generation of a new reference data set for the user, the QuantumGIS plug-in makes a corresponding shapefile available which has the right structure and can be complemented with area borders and their attributions. The function can be activated by clicking on ''Create reference layer template'', after the entire section ''Reference data import'' has been activated by using the corresponding check box. The following dialog requests the name of the file which is supposed to be generated (the default setting is <code><project name>_ref</code>) and asks the user to determine the estimated number of thematic classes. This indication is not final, subsequently classes can be added or removed - the only function is to make the determined number of classes (with different visual representation in terms of color) available for the definition of reference areas.

−	[[~~Bild~~:ilms_img_qgis_work_32.png\|Auswahldialog zur Erzeugung eines neuen Referenzdatensatzes mit voraussichtlicher Klassenanzahl]]	+	[[File:ilms_img_qgis_work_32.png\|Auswahldialog zur Erzeugung eines neuen Referenzdatensatzes mit voraussichtlicher Klassenanzahl]]

	In case the reference data set which should be generated already exists, the user will be informed and asked to make a decision:		In case the reference data set which should be generated already exists, the user will be informed and asked to make a decision:

−	[[~~Bild~~:ilms_img_qgis_work_33.png\|Benachrichtigung im Falle, dass der zu erzeugende Referenzdatensatz bereits existiert]]	+	[[File:ilms_img_qgis_work_33.png\|Benachrichtigung im Falle, dass der zu erzeugende Referenzdatensatz bereits existiert]]

	When the layer has been generated, it appears in the level overview of the current QuantumGIS project. There it can also be transferred into an editable mode by right-clicking on and selecting ''Bearbeitungsmodus umschalten'' (change editing mode). This is necessary to register the geometries of the training areas and their attribution into the reference dataset.		When the layer has been generated, it appears in the level overview of the current QuantumGIS project. There it can also be transferred into an editable mode by right-clicking on and selecting ''Bearbeitungsmodus umschalten'' (change editing mode). This is necessary to register the geometries of the training areas and their attribution into the reference dataset.

−	[[~~Bild~~:ilms_img_qgis_work_34.png\|Umschalten in den Bearbeitungsmodus]]	+	[[File:ilms_img_qgis_work_34.png\|Umschalten in den Bearbeitungsmodus]]

	Now the tool ''Polygon digitalisieren'' (digitize polygon) can be used to generate new reference areas. This usually happens when re-accessing original image data; according to the objective and classification scheme also other visualizations can deliver good indications for borders of training areas. The following image illustrates a detail of the generation of a reference area for the class ''Settlement/Siedlung''.		Now the tool ''Polygon digitalisieren'' (digitize polygon) can be used to generate new reference areas. This usually happens when re-accessing original image data; according to the objective and classification scheme also other visualizations can deliver good indications for borders of training areas. The following image illustrates a detail of the generation of a reference area for the class ''Settlement/Siedlung''.
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	<ol>		<ol>
	<li>First the support points of the polygon are generated by left-clicking at the corresponding coordinates. It is not necessary to be very precise since ILMSImage is able to separate potential peripheries from central areas of a reference area by statistic criteria. Instead of being accurate it often makes more sence to increase the sample by generating various reference areas for a thematic class. <br/><br/>		<li>First the support points of the polygon are generated by left-clicking at the corresponding coordinates. It is not necessary to be very precise since ILMSImage is able to separate potential peripheries from central areas of a reference area by statistic criteria. Instead of being accurate it often makes more sence to increase the sample by generating various reference areas for a thematic class. <br/><br/>
−	[[~~Bild~~:ilms_img_qgis_work_35.png\|400px\|Digitalisieren eines Referenzpolygons]]<br/><br/></li>	+	[[File:ilms_img_qgis_work_35.png\|400px\|Digitalisieren eines Referenzpolygons]]<br/><br/></li>
	<li>When all support points are set, a right-click finishes the digitization of the polygon. The following input mask the class ID and the corresponding description are indicated. It should be noted again that the numbering of classes starts from one and has to be consecutive.<br/><br/>		<li>When all support points are set, a right-click finishes the digitization of the polygon. The following input mask the class ID and the corresponding description are indicated. It should be noted again that the numbering of classes starts from one and has to be consecutive.<br/><br/>
−	[[~~Bild~~:ilms_img_qgis_work_36.png\|400px\|Maske zur Eingabe der Attribute]]<br/><br/></li>	+	[[File:ilms_img_qgis_work_36.png\|400px\|Maske zur Eingabe der Attribute]]<br/><br/></li>
	<li>After confirming the input mask, the polygon is shown in the color which is randomly selected for this ID.<br/><br/>		<li>After confirming the input mask, the polygon is shown in the color which is randomly selected for this ID.<br/><br/>
−	[[~~Bild~~:ilms_img_qgis_work_37.png\|400px\|Vollständiges Referenzgebiet in Zufallsfarbendarstellung]]<br/><br/></li>	+	[[File:ilms_img_qgis_work_37.png\|400px\|Vollständiges Referenzgebiet in Zufallsfarbendarstellung]]<br/><br/></li>
	<li>It is useful to modify the color of the representation according to the facts, so that either auto-plausibility is established (forest = green, water = blue) or the common usage of certain colors (settlement = red, agriculture = earth-colored) is applied. Using the above-described template means that no longer the entire representation of the layer has to be adjusted but only the color of individual thematic classes. The required window can be accessed by left-clicking on the color which is to be modified. There also the designation can be updated (from ''reference class 1'' to ''Settlement''). <br/><br/>		<li>It is useful to modify the color of the representation according to the facts, so that either auto-plausibility is established (forest = green, water = blue) or the common usage of certain colors (settlement = red, agriculture = earth-colored) is applied. Using the above-described template means that no longer the entire representation of the layer has to be adjusted but only the color of individual thematic classes. The required window can be accessed by left-clicking on the color which is to be modified. There also the designation can be updated (from ''reference class 1'' to ''Settlement''). <br/><br/>
−	[[~~Bild~~:ilms_img_qgis_work_38.png\|400px\|Fenster zur Änderung von Darstellungsfarbe und Beschreibung]]<br/><br/></li>	+	[[File:ilms_img_qgis_work_38.png\|400px\|Fenster zur Änderung von Darstellungsfarbe und Beschreibung]]<br/><br/></li>
	<li>The results is a first reference areas of a specific thematic class, the illustrated example shows the class ''Settlement/Siedlung''.<br/><br/>		<li>The results is a first reference areas of a specific thematic class, the illustrated example shows the class ''Settlement/Siedlung''.<br/><br/>
−	[[~~Bild~~:ilms_img_qgis_work_39.png\|400px\|Vollständiges Referenzgebiet in dem Sachverhalt angepasster Darstellung]]<br/><br/></li>	+	[[File:ilms_img_qgis_work_39.png\|400px\|Vollständiges Referenzgebiet in dem Sachverhalt angepasster Darstellung]]<br/><br/></li>
	</ol>		</ol>

	The above-described procedure is repeated until a sufficient number of reference areas has been defined. A repeated switching and confirming the following dialog window back up the reference dataset. If the colors and designations of the classes are modified as well, the example of the Rot shows the following image:		The above-described procedure is repeated until a sufficient number of reference areas has been defined. A repeated switching and confirming the following dialog window back up the reference dataset. If the colors and designations of the classes are modified as well, the example of the Rot shows the following image:

−	[[~~Bild~~:ilms_img_qgis_work_40.png\|800px\|Vollständig definierter Referenzdatenlayer überlagert über der Grenze des Untersuchungsgebiets]]	+	[[File:ilms_img_qgis_work_40.png\|800px\|Vollständig definierter Referenzdatenlayer überlagert über der Grenze des Untersuchungsgebiets]]

	===Importing Reference Areas===		===Importing Reference Areas===
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	In order to make the defined reference dataset suitable for thematic classification, the it has to be imported into the ILMSImage project. This means, first of all, that the data have to be loaded in the current QuantumGIS project - no matter how it has been generated. In the list box ''Reference data layer'' all loaded vector levels are listed which have the above-described structure and are therefore considered importable by ILMSImage. After the layer required has been selected, clicking on ''Conduct classification'' starts the actual import of reference data. This transforms the vector dataset into raster format and added to the attribute list of the cells. This is supposed to happen usually very fast. The complete result can be represented in the current map view by clicking on ''Visualize results''. The result for the above-described example of a reference dataset can be like this (subject to the underlying cell geometries):		In order to make the defined reference dataset suitable for thematic classification, the it has to be imported into the ILMSImage project. This means, first of all, that the data have to be loaded in the current QuantumGIS project - no matter how it has been generated. In the list box ''Reference data layer'' all loaded vector levels are listed which have the above-described structure and are therefore considered importable by ILMSImage. After the layer required has been selected, clicking on ''Conduct classification'' starts the actual import of reference data. This transforms the vector dataset into raster format and added to the attribute list of the cells. This is supposed to happen usually very fast. The complete result can be represented in the current map view by clicking on ''Visualize results''. The result for the above-described example of a reference dataset can be like this (subject to the underlying cell geometries):

−	[[~~Bild~~:ilms_img_qgis_work_41.png\|800px\|Importierter und visualisierter Referenzdatensatz]]	+	[[File:ilms_img_qgis_work_41.png\|800px\|Importierter und visualisierter Referenzdatensatz]]

	The process applies the colors used for visualization directly from the representation of the corresponding vector data layer. For a better differentiation of the thematic classes it is recommended to use different colors for them when they are defined. If they are auto-plausible or correspond to common associations (see above), a quick recognition of the reference dataset in raster format - and a subsequent result of the actual classification - is guaranteed.		The process applies the colors used for visualization directly from the representation of the corresponding vector data layer. For a better differentiation of the thematic classes it is recommended to use different colors for them when they are defined. If they are auto-plausible or correspond to common associations (see above), a quick recognition of the reference dataset in raster format - and a subsequent result of the actual classification - is guaranteed.
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	The final result of the classification is available in raster format as a GeoTIFF file to allow maximum comparability.		The final result of the classification is available in raster format as a GeoTIFF file to allow maximum comparability.

−	[[~~Bild~~:ilms_img_qgis_work_42.png\|800px\|Endgültige thematische Klassifikation des Untersuchungsgebiets]]	+	[[File:ilms_img_qgis_work_42.png\|800px\|Endgültige thematische Klassifikation des Untersuchungsgebiets]]