Image classification

Image classification

Classification methods in GRASS

	radiometric unsupervised	radiometric supervised 1	radiometric supervised 2	radiometric & geometric supervised
Image Preprocessing	r.seg
Preprocessing	i.cluster	i.class (monitor digitizing)	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 + v.to.rast (recommended)

Image Preprocessing (optional)

• r.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.seg documentation

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.

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.

Unsupervised classification

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

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

Supervised classification

• i.smap - Performs contextual (image segmentation) image classification using sequential maximum a posteriori (SMAP) estimation.
• In case of panchromatic maps or limited amount of channels, it is often recommended to generate synthetic channels through texture analysis (r.texture)

Small classification tutorial

Required: GRASS >= 6.4.0

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.rgb b=lsat7_2002_70 g=lsat7_2002_50 r=lsat7_2002_10
 d.rast lsat7_2002_class3 cat=1 -o

Supervised classification

Rlaunch GUI for digitizer:

 g.gui wxpython

 # ... digitize training areas of water, asphalt, forest, uncovered soil; polygons/centroids with attribute
 # advantage: we know what the classes are

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:

 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

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

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 :)

Further reading classification with GRASS

• 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