View source for Object-based classification (Tutorial)


== Determining segmentation parameters ==

== Mean-shift segmentation of large size images ==
# Step. We perform a mean shift filtering on the preprocessed input image ''188_pca_cmask_8b.tif''. (3 principal components of Landsat-8 multispectral bands (B1 - B7), pansharpened with band B8, rescaled to 8bit. Clouds and cloud shadows are masked). We extract homogeneous objects on the basis of a filtered image. Go to {{mitem|text=Processing-Toolbox --> Orfeo Toolbox (image analysis) --> Image Filtering --> Exact Large-Scale Mean-Shift segmentation, step 1 (Smoothing)}}. 
* Set the ''188_pca_cmask_8b'' layer as {{button|text=Input image}}.
* The {{button|text=Range radius}} should be set to 30.
* Leave all other configurations as they are and click {{button|text=Run}}.
Have a look on the resulting the resulting filtered and spatial image.
# Step. Go to {{mitem|text=Learning --> Object Labeling}}, in the field {{button|text=The image to classify}} select file generated in the previous step and in the field {{button|text=The segmentation of the image}} select file {{mitem|text=MeanShift1 --> Labeled Image}} and press {{button|text=OK}} {{red|text=Correct? [[User:Lburgr|- Levent]] ([[User talk:Lburgr|talk]])}}. A window {{mitem|text=Object Labeling}} opens. 
# Click Add to start object labeling. A new class appears in the {{button|text=Objects}} tab in the upper right corner of the window. Specify color red for {{button|text=New Class 0 = Urban}}.  Double right click the image to select {{typed|text=10}} urban objects as visually intepreted. Repeat the same procedure for {{button|text=New Class 1 = Cropland}} in yellow color and {{button|text=New Class 2 = Forest}} in green color.
# To select shape and pixel parameters for object clustering: In the {{button|text=Features}} tab check {{button|text=STATS::Band1::Mean}}, {{button|text=STATS::Band2::Mean}}, {{button|text=STATS::Band3::Mean}} {{red|text=-- are these really the names of the boxes? [[User:Lburgr|- Levent]] ([[User talk:Lburgr|talk]])}}
# To selecti Support Vector Machine (SVM) parameters: Leave the other parameters to default and check the {{button|text=Parameters optimization}} box. Click {{button|text=classify}} to obtain the output displayed in Figure '''B'''. Using the {{button|text=Opacity}} slide, you can compare the output with the original image. Note the accuracy measure in the lower right side of the window. 
# Try other Features by checking them in the {{button|text=Features}} tab and compare output clusters with the output of parameters specified at step 3. 

{{info|message=Info|text=For more detailed information on the SVM algorithm visit the [http://www.csie.ntu.edu.tw/~cjlin/libsvm/ library website]}}

[[Image:Monteverdi_Object_Labeling.jpg|thumb|center|450px|'''Figure B''': Object labeling menu in Monteverdi]]

== Object-based classifcation using SVM algorithm ==
# Load the raster map ''188_pca_indices_pan.tif'' provided in the [[course data]]. Open {{button|text=Mean Shift Segmentation (Large scale, vector output)}} from the Orfeo toolbox algorithms in the {{button|text=Processing}} bar. 
# Select ''188_pca_indices_pan.tif'' as input image. Change {{button|text=Range radius}} to 0.5 and {{button|text=Min Region size}} to 50. Specify the output file as ''Segments_all.shp'' and click {{button|text=RUN}}. 
# Load ''Segments_all.shp'' to the project. To view both the vector segments and the underlying image file, visualize the segments as hollow polygons: Right click on ''Segments_all.shp'' in the [[TOC]],  and select {{mitem|text=Properties --> Style}} and select {{button|text=Fill-Simple fill}} with {{button|text=No brush}} as {{button|text=Fill style}}. Change the border outline colour to white for better visibility (see figure '''C''').
# Split the multiband image ''188_pca_indices_ pan_mean.tif'' to single band rasters. This step is necessary to allow extraction of individual segment spectral values using the zonal statistics tool as shown in step 5: Open {{mitem|text=Split image}} tool from the Orfeo toolbox. Select {{mitem|text=Image manipulation --> Split image}} and specify output image. 
# To extract pixel spectral data for each object using the Zonal statistics Plugin: Install Zonal Statistics Plugin from the {{mitem|text=Plugins --> Manage and Install plugins}} menu. Select {{button|text=Get more}} and browse for the {{button|text=Zonal statistics plugin}}. After successfully installing the tool, open it from {{mitem|text=Raster --> Zonal statistics --> Zonal statistics}} and specify the Raster layer, Polygon layer containing the zones and Output column for Bands 1, 2 and 3 respectively.
# Rasterize the segments to prepare for classification: Open the Rasterization tool from the Orfeo tool box, selecting {{mitem|text=Vector Data Manipulation --> Rasterization}}. Select ''Segments_all.shp'' as {{button|text=Input vector dataset}}. Select ''188_pca_indices_pan_mean.tif'' as {{button|text=Input reference image}}. Also enter the input coordinates  {{button|text=Output Upper-left x}} and {{button|text=Output Upper-left y}}. The coordinates can be found in the {{button|text=Metadata}} tab of the layer {{button|text=Properties}}. Change {{button|text=Spacing (GSD)x}} and {{button|text=Spacing (GSD)y}} spacing to 15 and {{button|text=Rasterization mode}} to attribute. Specify the output file and click {{button|text=RUN}}. 
# To collect training objects for the Object based Supervised Maximum Likelihood Classification: Select 210 random objects from ''Segment_all.shp'' using the random selection tool {{mitem|text=Vector --> Research Tools --> Random selection}}. Set {{button|text=Number of Features}} to 210. Press {{button|text=OK}}. Right click the layer ''Segment_all.shp'' and {{button|text=Save selected objects}} as ''210_selected.shp''. Load the new shapefile into QGIS.  
# Specify the class for each object in ''210_selected.shp'' file according to table '''A''' {{red|text=Where do I find this? [[User:Lburgr|- Levent]] ([[User talk:Lburgr|talk]])}}. This is the training dataset for maximum likelihood classification. Load a Google Maps Image from the OpenLayers plugin: {{mitem|text= Plugins --> OpenLayers plugin --> Add Google Satellite layer}}. Install the {{button|text=QuickMultiAttributeEdit}} plugin. The tool simply helps in editing attributes of a vector file without opening the attribute table. Open the tool from {{mitem|text=Plugins --> QuickMultiAttributeEdit}}.  Right click ''210_selected.shp'' and open the attribute table. Toogle the editing mode. Select one row i.e. one segment by clicking {{button|text=Zoom map to the selected row}}, identify the land use type by corroborating with google imagery. Press {{button|text=F12}} key to open the {{button|text=QuickMultiAttributeEdit}} and set the {{button|text=IDClass}} corresponding to the Class Code in Table '''A'''. Repeat this procedure for each segment.
# Perform maximum likelihood classification of the object segments using the {{button|text=Semiautomatic classification plugin}}: To open the plugin go to {{mitem|text=Raster --> Semi-Automatic Classification Plugin --> Semi-Automatic Classification Plugin}}. Click {{button|text=Show docks}}. In the {{button|text=Semi-Automatic:ROI creation}} window select {{button|text=input}} as 188_pca_indices_ pan_mean.tif., {{button|text=Select a training shapefile}} as train_segments_6class.shp. Leave the ROI parameters to default. In the Semi-Automatic: Classification window {{button|text=select classification algorithm}} to Maximum Likelihood, {{button|text=select qml}} as ''classification.qml''. Check {{button|text=Create vector}}, {{button|text=Apply mask}}, {{button|text=Classification report}} and {{button|text=Calculate accuracy}}. Specify a cloud mask as ''cloud_mask.shp''. Click {{button|text=Perform Classification}} and specify the output file as ''semi_automatic classification.shp''. An additional output file ''semi_automatic classification.tif'' will be created.  See Figure '''D'''. Import the classification style from ''classification.qml''.  To conduct an accuracy assessment for your map output, go to the {{button|text=Post processing}} tab and select {{button|text=Accuracy}} in the Semi-Automatic Classification Plugin window.  Load ''semi_automatic_classification.tif'' in {{button|text=Select the classification to assess}} and ''train_segments_6class.shp'' in {{button|text=Select the reference shapefile}} and click {{button|text=Calculate error matrix}} (see figure '''E'''). {{red|text=We did all of this in [[Exercise 08: Supervised classification|ecercise 08]]. Shouldn't we just link there? [[User:Lburgr|- Levent]] ([[User talk:Lburgr|talk]])}}

{|class="wikitable" style="border:0pt"
|style="border:0pt"|[[Image:RemSens_Exercise10_01.jpg|thumb|450px|'''Figure C''': Result of segmentation by mean shift clustering with the Orfeo Toolbox Mean Shift Segmentation algorithm]]
|style="border:0pt"|[[Image:RemSens_Exercise08_04.png|thumb|450px|'''Figure D''': Result of the semi-automatic maximum likelihood classification]] 
|-
|style="border:0pt"|[[Image:RemSens_Exercise08_05.png|thumb|450px|'''Figure D''': Error matrix for the semi-automatic maximum likelihood classification]] 
|}

== Alternative supervised classification with Support Vector Machine (SVM) ==
# Start Monteverdi.
# Open ''188_pca_indices_pan_mean.tif'' and ''train_segments_7class.shp'' in Monteverdi via {{mitem|text=File --> Open dataset}}. 
# Go to {{mitem|text=Learning --> SVM classification (EXPERIMENTAL)}}
# Load ''188_pca_indices_pan_mean.tif'' as {{button|text=Input image}} and ''train_segments_7class.shp'' as {{button|text=Training/validation ROIs}} and press {{button|text=OK}}.
# Specify the {{button|text=Class key}} as {{button|text=ID_class}} and click {{button|text=Train}}. A classification output is seen Monteverdi.
# Click {{button|text=Validate}} to see classification accuracy and {{button|text=Calculate error matrix}}.  See Figure '''G'''.

[[file:Monteverdi_Supervised_Error_Matrix.jpg|thumb|center|450px|Supervised classification menu in Monteverdi with error matrix]]

[[Category:Image classification]]