Semi-Automatic Classification Plugin- Tutorial

7/23/2019 Semi-Automatic Classification Plugin- Tutorial

http://slidepdf.com/reader/full/semi-automatic-classification-plugin-tutorial 1/20

12/9/2015 3. Tutor ials — Sem i- Autom atic C lassification Plugin 2.5.1 docum entation

http://semiautomaticclassificationmanual.readthedocs.org/en/latest/Tutorials.html 1/20

3. Tutorials

The following is a basic tutorial about the use of the Semi-Automatic Classification

Plugin. However, visit the blog From GIS to Remote Sensing for new and updated

tutorials such as:

Estimation of Land Surface Temperature with Landsat Thermal Infrared Band;

Land Cover Classification of Cropland.

In this tutorial we are going to classify a Landsat image (single band rasters). Download

the sample dataset, which is a Landsat 8 image (a subset acquired in the South of Rome,

Italy) available from the U.S. Geological Survey. The following bands (each band is a

single 16 bit raster) are included in the file:

2 - Blue;

3 - Green;

4 - Red;

5 - Near-Infrared;

6 - Short Wavelength Infrared 1;

7 - Short Wavelength Infrared 2.

The Semi-Automatic Classification Plugin uses SAGA GIS for the classification process.

The SAGA algorithms work only with single band images as input. Therefore, if input is amulti band raster (i.e. a single image file made of several bands), the Semi-Automatic

Classification Plugin automatically splits the input file to single band rasters, which takes

some time depending on raster size. In order to optimize the classification process

(especially for hyperspectral images), it is preferable to use single band rasters, or split

the image file to single bands, as explained here (point 1) .

3.1. Define the inputs

Required inputs are a multi band raster or single band rasters, and a training shapefile

for ROI creation. In this tutorial we are going to create a band set.

Start QGIS and load the raster bands; create a color composite (RGB=543) of the

sample data, which is useful for the photo interpretation, as explained here (point

2); start the Semi-Automatic Classification Plugin (if the docks of the are not

displayed, click the button Show docks in the main plugin interface);

http://fromgistors.blogspot.com/2013/07/land-cover-classification-update.html

http://fromgistors.blogspot.com/2013/07/working-with-multispectral-bands-in-qgis.html

http://hub.qgis.org/attachments/download/5930/Sample_data_Landat8_bands.zip

http://fromgistors.blogspot.com/search/label/Tutorial

http://fromgistors.blogspot.com/2013/07/land-cover-classification-update.html

http://fromgistors.blogspot.com/2013/07/working-with-multispectral-bands-in-qgis.html

http://hub.qgis.org/attachments/download/5930/Sample_data_Landat8_bands.zip

http://fromgistors.blogspot.com/2014/01/land-cover-classification-of-cropland.html

http://fromgistors.blogspot.com/2014/01/estimation-of-land-surface-temperature.html

http://fromgistors.blogspot.com/search/label/Tutorial





In the plugin dock ROI creation click the button Band set besideSelect an image;

the tab Band set will open; click the button Select All, then Add rasters to set;

under Band set definition, order the band names in ascending order, from top to

bottom using the arrows (this is useful for the interpretation of spectral

signatures);





Now we need to create a training shapefile that will store ROIs (a polygon layer

that requires two fields ID_class and ROI_info in the attribute table); in the dock

ROI creation click the button New shapefile, and select where to save the shapefile

(for instance ROI.shp).





Hint: in order to use any shapefile (having different field names for ID and Information)

as training shapefile, it is possible to change the default field names in the tab Settings

of the main interface, according to the existing field names. When needed, the plugin

will add automatically other fields for the spectral signature calculation.

3.2. Create ROIs

ROIs are polygons delimiting a certain land cover class, which are required to train the

classification algorithm. Created ROIs are temporary polygons, which are placed inside

the group Class_temp_group (added automatically if missing) in the Layers panel. This

group should be moved to the bottom of the Layers panel; this way, only the last

created ROI will be displayed over the image (the previous ROIs will be automatically

hidden).

In order to create a ROI, in the dock ROI creation click the button + beside Create

a ROI (this activates the ROI pointer); ROIs are created by clicking on any pixel of

the image; click on the dark area in the South, which is the Lake Albano (use the

mouse scroll to zoom); after a few seconds the ROI polygon will appear over the

image (a semitransparent orange polygon);





It is possible to change the ROI creation parameters, in order to create a smaller

or lager ROI; change the Min ROI size value to 100, and the Max ROI width value to

150; in order to create a ROI at the same point clicked before, click the button

Redo ROI;

Now, check Rapid ROI on band and click the button Redo ROI; this way, ROIs can

be computed only on one selected band, saving time especially forhyperspectral images; change the value to 4 beside Rapid ROI on band, and click

the button Redo ROI; as you can see, ROIs have different shapes depending on the

selected band;





Open the Settings tab of the main interface; under ROI style it is possible to

change colour and transparency of created ROIs as you wish.

3.3. Save the ROIs to shapefile

This step is required in order to save the created ROIs to the training shapefile.

Under ROI definition type a brief description of the ROI inside the field Information

(e.g. water); this description will not be used during the classification process, but it

is useful for distinguishing ROIs; the field ID (i.e. identifier of the class) is used as

reference for the land cover classification, therefore it is important that each

category has a unique ID value; ROIs sharing the same ID are treated as a single

ROI; now, leave the ID set to 1;

If the checkbox Calculate signature is checked, then the ROI spectral signature is

calculated while the ROI is saved; now, uncheck this checkbox (we are going to

calculate it later, from the Spectral signature tab);

In order to save the ROI to the training shapefile click the button Save ROI to

shapefile;

It is possible to delete the last saved ROI by clicking Undo save ROI.

3.4. Spectral signature plot

In the plugin main interface, select the tab ROI tools > Spectral signature, which

displays the plots of selected ROIs, and select the item water; it is possible to

calculate the ROI signature by clicking the button Calculate signature and

confirming;

If the checkox Plot σ is checked, then the plot will display the standard deviation





of each ROI; you can pan and zoom through the plot using the navigation toolbar

(provided by Matplotlib).

Hint: in the tab Settings of the main interface, it is possible to change the maximum

number of characters for ROIs of the plot legend (which is 15 by default).





3.5. Scatter plot

The Scatter plot tab allows for the calculation of the ROI scatter plots, which are useful

to assess ROI separability choosing between two bands. Pixel values for two raster

bands are represented as points in the 2D space.

3.6. Multiple ROI creation at onceIt is possible to create automatically ROIs given a list of point coordinates (X,

Y), class ID and ROI information (ROIs are created with the parameters defined in

the dock ROI creation); select the ROI tools tab > Multiple ROI creation and click

Add point to add a new line where you can fill the required fields; now select the

line (click the line number on the left) and click Remove highlighted points;





It is also possible to import a list of points, maybe from field survey; download

this text file, click the button Import and select the downloaded file;

http://hub.qgis.org/attachments/download/6176/sample_points.txt





In order to create and save the ROIs to the shapefile click Create and save ROIs (it

takes some time, depending on the number of points).





3.7. Perform a classification preview

Classification preview is a rapid way to evaluate collected ROIs.

It is possible to choose between several classification algorithms (Maximum

Likelihood; Minimum Distance; Spectral Angle Mapping); now select Spectral Angle

Mapping;Algorithm threshold allows you to leave unclassified pixels that meet a certain

rule: for Maximum Likelihood, pixels are unclassified if probability is less than

threshold (max 100); for Minimum Distance, pixels are unclassified if distance is

greater than threshold; for Spectral Angle Mapping, pixels are unclassified if

spectral angle distance is greater than threshold (max 90);

Preview size is the side of the classification preview (in pixel unit); setSize to 300;

click the button + under Classification preview and click on the image; similarly to

the ROI, click the button Redo to perform another classification preview at the

same point;

Created ROIs are displayed in the ROI list; double click on any item to zoom to the

corresponding ROI in the map; also, it is possible to delete selected ROIs using

the button Delete selected ROIs.





3.8. Set a classification style

It is useful to create a classification style with labels that will be loaded for every

classification.

In the panel Layers, left click on a classification preview and selectProperties;

change the colours and labels of classes, according to the training ROIs; then,create click Save style ... to save the .qml file (e.g. style.qml);

In the Classification dock, under Classification style click the button Select qml to

select the file style.qml; the next classification will be loaded with this style.





Hint: after adding a new class to the training shapefile, repeat the above steps to

overwrite the .qml file and update the classification style.

A large number of ROIs is required for a good classification. Download this training

shapefile , which contains several ROIs, and load it in QGIS.

The main output of a classification is a raster file .tif; click the button Perform

classification and select where to save the output (e.g. classification.tif); use theclassification style from this file zip ;

In addition to this raster, it possible to create the shapefile of the classification

by checking the checkbox Create vector; the shapefile will be saved with the same

name and in the same directory of the .tif file (it takes some minutes for the vector

creation, depending on your system spec);

http://hub.qgis.org/attachments/download/6004/classification.zip

http://hub.qgis.org/attachments/download/6004/classification.zip





If the checkbox Calculate accuracy is checked, than the error matrix is calculated

and saved as file .txt in the same directory of the .tif file (also, it is automatically

displayed in the tab of the plugin main interface Post processing > Accuracy); the

error matrix is calculated by comparing the classification to the training shapefile

used for the classification (see below Post processing tools);

It is possible to apply a mask shapefile to the classification; download this

shapefile , check the checkbox Apply mask and select the downloaded shapefile;click the button Perform classification, and the classification will be saved along

with the error matrix and the vector output.

http://hub.qgis.org/attachments/download/6113/Mask_shapefile.zip





3.9. Post processing tools

It is possible to assess the classification accuracy (implemented by GRASS GIS),

by comparing the classification to a reference shapefile (not necessarily the

training shapefile); now, select the tab Post processing > Accuracy of the plugin

main interface; select theclassification.tif beside Select a classification to assess

and select the ROI shapefile beside Select the reference shapefile; then click thebutton Calculate error matrix and the matrix will be displayed; you can save the

error matrix by clicking the button Save error matrix to file;

It is useful to calculate the land cover change (through GDAL and Numpy)

between a reference classification raster and a new classification raster; download

this classification (pretend this is the last year classification); select the tab Postprocessing > Land cover change of the plugin main interface, select the

downloaded classification as reference classification, and the classification.tif as

the new classification; click the button Calculate land cover change and select

where to save the raster of changes and the related table (i.e. a file .csv, whose

values are separated by tab); pixel values of the raster of changes (ChangeCode)

are described in the table, and each value represent a class of change from the

reference classification to the new classification;

http://hub.qgis.org/attachments/download/6180/reference_classification.zip









If the checkbox Mask unchanged pixel is checked, then unchanged pixels will have

a value of 0 (Unclassified).









3.10. Advanced settings

It is worth mentioning also other advanced settings:

In the tab Settings of the plugin main interface it is possible to set the RAM used

for processing; set the Available RAM according to your computer spec (in

general, half of the system RAM is a good value);

if the checkbox Record events in a Log file is checked a Log file is created and

updated during the processes; the Log file is saved in the plugin directory (located

inside your user/home directory,

.qgis2/python/plugins/SemiAutomaticClassificationPlugin ), with the name

__0semiautomaticclass.log;several buttons allow for the testing of program installation such as: SAGA GIS,

GRASS GIS, and QGIS geoalgorithms;




For more and updated tutorials please visit http://fromgistors.blogspot.com .

http://fromgistors.blogspot.com/

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Semi-Automatic Classification Plugin- Tutorial