Image classification

Image classification

Classification methods in GRASS

	radiometric unsupervised	radiometric supervised 1	radiometric supervised 2	radiometric & geometric supervised
Image Preprocessing	r.smooth.seg (optional, in case of noisy original data)
Segmentation	i.segment
Preprocessing	i.cluster	i.class (monitor digitizing), g.gui.iclass (in GRASS 70)	i.gensig (using training maps)	i.gensigset (using training maps)
Classification	i.maxlik	i.maxlik	i.maxlik	i.smap
Remarks	automated run based	requires digitalization	requires digitalization	requires digitalization
	on image statistics	of training areas	of training areas	of training areas

You can digitize training areas with either r.digit (not recommended) or v.digit GRASS Digitizing tool + v.to.rast (recommended)

Image Preprocessing (optional)

• r.smooth.seg - produces a smooth approximation of the data and performs discontinuity detection. The module is based on the Mumford-Shah variational model for image segmentation. Used as pre-analysis for a subsequent classification.

For details, see the r.smooth.seg manual (formerly called: r.seg)

• i.segment - identifies segments (objects) from imagery data based (currently) on a region growing and merging algorithm. The image segmentation results can be useful (on their own or) as a preprocessing step for image classification, i.e. reduce noise and speed up the classification.
  • Classification of these segments: see below in "Unsupervised classification".

Interactive setup

• i.class - Generates spectral signatures for an image by allowing the user to outline regions of interest.

The resulting signature file can be used as input for i.maxlik or as a seed signature file for i.cluster.

• g.gui.iclass - Tool for supervised classification of imagery data.

Preprocessing

• i.cluster - Generates spectral signatures for land cover types in an image using a clustering algorithm.

The resulting signature file is used as input for i.maxlik, to generate an unsupervised image classification.

• i.gensig - Generates statistics for i.maxlik from raster map layer.
• i.gensigset - Generate statistics for i.smap from raster map layer.

Background information concerning i.cluster:

• Discussion: Is "i.cluster" an implementation of the ISODATA algorithm?
• About Clustering

Unsupervised classification

For an introduction, see for example Cluster_analysis.

• i.maxlik - Classifies the cell spectral reflectances in imagery data.

Classification is based on the spectral signature information generated by i.cluster (see above)

• GRASS GIS 7: i.segment
  • Classification of these segments could be achieved with these addons: v.class.mlR, v.class.mlpy, v.class.ml.

See example below.

Supervised classification

• i.maxlik - Classifies the cell spectral reflectances in imagery data.

Classification is based on the spectral signature information generated by either i.class, or i.gensig.

• g.gui.iclass - Tool for supervised classification of imagery data.
• i.smap - Performs contextual (image segmentation) image classification using sequential maximum a posteriori (SMAP) estimation.
• r.learn.ml - Supervised Machine Learning classification and regression of GRASS rasters using the python scikit-learn package
• In case of panchromatic maps or limited amount of channels, it is often recommended to generate synthetic channels through texture analysis (r.texture)

See example below.

Object-based classification and image segmentation

Created based on Summary of object-based classification possibilities in GRASS GIS (June 2014). Feel free to merge this with other content or with different page.

Related commands:

• i.segment - Identifies segments (objects) from imagery data.
• i.segment.hierarchical performs a hierarchical segmentation on an image (group). The module uses internally i.segment
• i.segment.stats calculates statistics describing raster area.
• extra: r.smooth.seg - Generates a piece-wise smooth approximation of the input raster and a discontinuity map.
• extra: i.superpixels.slic - Perform image segmentation using the SLIC segmentation method.

Background information:

• Publications on GRASS GIS and Object Based Image Analysis (OBIA) techniques (via Google Scholar)
• Old references
  • Moritz's steps and the main discussion: Object-based image classification in GRASS (nabble link)
  • Moritz's steps in a nice guide by Martin, in Czech with images (English Google-translation)
  • Alternative notes by Moritz: Classification of segments (nabble link)
  • Pietro's steps and tools (nabble link)
• Another discussion about what variables to use: Object-based classification (nabble link)

Source code and scripts:

• The source code of the classification tools (for inspiration in writing your own scripts):
  • v.class.ml: http://svn.osgeo.org/grass/grass-addons/grass7/vector/v.class.ml/ (Pietro's tool)
  • v.class.mlpy: http://svn.osgeo.org/grass/grass-addons/grass7/vector/v.class.mlpy/ (Vaclav's tool)
  • i.mlpy.py: http://svn.osgeo.org/grass/sandbox/turek/i.mlpy.py (Stepan's tool)
• List of Python machine learning libraries:
  • scikit-learn, http://scikit-learn.org/ (used by v.class.ml)
  • mlpy, http://mlpy.sourceforge.net/ (used by v.class.ml, v.class.mlpy and i.mlpy)
  • Pandas, http://pandas.pydata.org/
  • Milk, http://luispedro.org/software/milk

Small classification tutorial

Required: GRASS >= 6.4

We are using Landsat satellite data prepared for the North Carolina sampling site. It is a multispectral dataset where each channel is stored in a separate map.

We are using the North Carolina location here.

Warming up with Landsat TM

 # look what maps are there
 g.list rast
 # set to one of the color channels and print current region settings
 g.region rast=lsat7_2002_10 -p

 # look at some Landsat map (1: blue, 2: green, 3: red, 4: near infrared etc.)
 d.mon x0
 d.rast lsat7_2002_10
 d.rast lsat7_2002_20
 d.rast lsat7_2002_30 
 d.rast lsat7_2002_40

We can easily query the spectrum for individual points:

 d.what.rast lsat7_2002_10,lsat7_2002_20,lsat7_2002_30,lsat7_2002_40

Histogram of a channel:

 d.histogram lsat7_2002_10

Create RGB view on the fly, true and false color:

 d.rgb b=lsat7_2002_10 g=lsat7_2002_20 r=lsat7_2002_30
 d.rgb b=lsat7_2002_70 g=lsat7_2002_20 r=lsat7_2002_30
 d.rgb b=lsat7_2002_70 g=lsat7_2002_50 r=lsat7_2002_30
 d.rgb b=lsat7_2002_70 g=lsat7_2002_50 r=lsat7_2002_10

Check thermal channel (encoded to fit into 0.255 data range):

1. http://landsathandbook.gsfc.nasa.gov/handbook/handbook_htmls/chapter11/chapter11.html
2. Look up LMIN/LMAX: "Table 11.2 ETM+ Spectral Radiance Range watts/(meter squared * ster * µm)"
3. we won't go into details here, r.mapcalc would be used to convert to Kelvoin/degree Celsius.

 # look at encoded thermal channel
 d.rast lsat7_2002_61

NDVI as example for map algebra:

 r.mapcalc "ndvi = 1.0 * (lsat7_2002_40 - lsat7_2002_30)/(lsat7_2002_40 + lsat7_2002_30)"
 d.rast.leg ndvi
 r.colors ndvi color=ndvi
 d.rast.leg ndvi

Show selected NDVI "classes":

 d.rast ndvi val=0.3-1.0
 d.rast ndvi val=-1.0--0.1
 d.rast ndvi val=0.0-0.2
 d.rast ndvi val=0.0-0.3

Look at panchromatic channel, compare resolution:

 g.region rast=lsat7_2002_80 -p
 d.erase -f
 d.rast lsat7_2002_80
 d.zoom
 d.rast lsat7_2002_30
 d.rast lsat7_2002_80

Image Classification

Set region setting to red image:

 g.region rast=lsat7_2002_30 -p

Create a group

 i.group group=lsat7_2002 subgroup=lsat7_2002 \
   input=lsat7_2002_10,lsat7_2002_20,lsat7_2002_30,lsat7_2002_40,lsat7_2002_50,lsat7_2002_70

Unsupervised classification

Generate unsupervised statistics

 i.cluster  group=lsat7_2002 subgroup=lsat7_2002 sigfile=lsat7_2002_sig classes=10 reportfile=lsat7_2002.txt

 # look at report file
 gedit lsat7_2002.txt 

Assign pixels to classes, check quality of assignment:

 i.maxlik group=lsat7_2002 subgroup=lsat7_2002 sigfile=lsat7_2002_sig class=lsat7_2002_class reject=lsat7_2002_reject

 # false color composite
 d.mon x0 ; d.font Vera
 d.rgb b=lsat7_2002_70 g=lsat7_2002_50 r=lsat7_2002_10
 # classification result
 d.mon x1 ; d.font Vera
 d.rast.leg lsat7_2002_class
 # rejection map
 d.mon x2 ; d.font Vera
 d.rast.leg lsat7_2002_reject
 # rejection histogram
 d.mon x3 ; d.font Vera
 d.histogram lsat7_2002_reject

NC Landsat map 2002 - false color composite

unsupervised classification

unsupervised classification rejection map histogram

unsupervised classification rejection map

Play with different cluster separations:

 i.cluster group=lsat7_2002 subgroup=lsat7_2002 sigfile=lsat7_2002_sig classes=10 reportfile=lsat7_2002.txt separation=1.5
 i.maxlik group=lsat7_2002 subgroup=lsat7_2002 sigfile=lsat7_2002_sig class=lsat7_2002_class2 reject=lsat7_2002_reject2
 d.mon x4
 d.rast.leg lsat7_2002_class2
 d.rast.leg lsat7_2002_reject2
 d.rast.leg lsat7_2002_reject
 d.rast.leg lsat7_2002_reject2
 d.rast.leg lsat7_2002_reject
 d.rast.leg lsat7_2002_reject2
 d.rast.leg lsat7_2002_class2

Compare to RGB:

 d.rgb b=lsat7_2002_70 g=lsat7_2002_50 r=lsat7_2002_10
 d.rast lsat7_2002_class2 cat=1 -o
 d.rgb b=lsat7_2002_70 g=lsat7_2002_50 r=lsat7_2002_10
 d.rast lsat7_2002_class cat=1 -o
 d.rgb b=lsat7_2002_70 g=lsat7_2002_50 r=lsat7_2002_10
 d.rast lsat7_2002_class cat=1 -o

We do a 3rd attempt with more classes

 i.cluster group=lsat7_2002 subgroup=lsat7_2002 sigfile=lsat7_2002_sig classes=15 reportfile=lsat7_2002.txt 
 i.maxlik group=lsat7_2002 subgroup=lsat7_2002 sigfile=lsat7_2002_sig class=lsat7_2002_class3 reject=lsat7_2002_reject3
 d.rast.leg lsat7_2002_class3

unsupervised classification with 15 classes

Supervised classification

Launch GUI for digitizer:

 g.gui wxpython

Now digitize training areas of water, asphalt, forest, uncovered soil on RGB composite in GUI. Digitize polygons/centroids with attributes. The advantage: we know what the classes are.

NC Landsat map 2002 - training areas

Assign also numerical ID for each class in new column

 v.db.select lsat7_training
 v.db.addcol lsat7_training col="id integer"
 v.db.select lsat7_training
 v.db.update lsat7_training col=id where="name = 'water'" val=1
 v.db.update lsat7_training col=id where="name = 'forest'" val=2
 v.db.update lsat7_training col=id where="name = 'asphalt'" val=3
 v.db.update lsat7_training col=id where="name = 'soil'" val=4
 v.db.select lsat7_training

Transform vector training map to raster model:

 g.region vect=lsat7_training align=lsat7_2002_10 -p
 v.to.rast in=lsat7_training out=lsat7_training use=attr col=id labelcol=name --o
 
 d.mon x0
 d.rast.leg lsat7_training 

Generate statistics from training areas

 i.gensigset group=lsat7_2002 subgroup=lsat7_2002 sig=lsat7_2002_smap training=lsat7_training

Perform supervised classification

 i.smap group=lsat7_2002 subgroup=lsat7_2002 sig=lsat7_2002_smap  out=smap

Colorize:

 r.colors smap rules=- << EOF

1 aqua
2 green
3 180 180 180
4 brown
EOF

 d.rast.leg smap
 d.rgb b=lsat7_2002_10 g=lsat7_2002_20 r=lsat7_2002_30

NC Landsat map 2002 - false color composite

SMAP supervised classification

Vectorize result:

 r.to.vect smap out=smap feat=area
 d.rgb b=lsat7_2002_10 g=lsat7_2002_20 r=lsat7_2002_30
 d.vect smap  type=boundary col=red

That's it :)

Assessing the quality and result of the classification

• r.cross
• r.kappa
• r.change.info: Landscape change assessment

Further reading on classification with GRASS GIS

• Georganos, S., Lennert, M., Grippa, T., Vanhuysse, S., Johnson, B., Wolff, E., 2018. Normalization in Unsupervised Segmentation Parameter Optimization: A Solution Based on Local Regression Trend Analysis. Remote Sensing 10, 222. https://doi.org/10.3390/rs10020222
• Grippa, T., Lennert, M., Beaumont, B., Vanhuysse, S., Stephenne, N., Wolff, E., 2017. An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification. Remote Sensing 9, 358. https://doi.org/10.3390/rs9040358
• Beaumont, B., Grippa, T., Lennert, M., Vanhuysse, S.G., Stephenne, N.R., Wolff, E., 2017. Toward an operational framework for fine-scale urban land-cover mapping in Wallonia using submeter remote sensing and ancillary vector data. JARS, JARSC4 11, 036011. https://doi.org/10.1117/1.JRS.11.036011
• Micha Silver: Analyzing acacia tree health in the Arava with GRASS GIS
• Perrygeo: Impervious surface deliniation with GRASS
• Dylan Beaudette: Working with Landsat Data
• Dylan Beaudette: Canopy Quantification via Image Classification
• Segmentazione o classificazione d'immagini con GRASS (in italiano)
• Di Palma, F.; Amato, F.; Nolè, G.; Martellozzo, F.; Murgante, B. 2016: A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation. ISPRS Int. J. Geo-Inf. 2016, 5, 109. PDF: http://www.mdpi.com/2220-9964/5/7/109

Further reading on OBIA with GRASS GIS

• A complete toolchain for object-based image analysis with GRASS GIS
  • Abstract: https://frab.fossgis-konferenz.de/en/foss4g-2016/public/events/1289
  • Video: https://av.tib.eu/media/20409
  • Software: https://github.com/tgrippa/Opensource_OBIA_processing_chain