NAME¶
imageryintro - Image processing introduction
Image processing introduction
Image processing in GRASS GIS¶
General introduction¶
Digital numbers and physical values (reflection/radiance-at-sensor):
Satellite imagery is commonly stored in Digital Numbers (DN) for minimizing the
storage volume, i.e. the originally sampled analog physical value (color,
temperature, etc) is stored a discrete representation in 8-16 bits. For
example, Landsat data are stored in 8bit values (i.e., ranging from 0 to 255);
other satellite data may be stored in 10 or 16 bits. Having data stored in DN,
it implies that these data are not yet the observed ground reality. Such data
are called "at-satellite", for example the amount of energy sensed
by the sensor of the satellite platform is encoded in 8 or more bits. This
energy is called radiance-at-sensor. To obtain physical values from DNs,
satellite image providers use a linear transform equation (y = a * x + b) to
encode the radiance-at-sensor in 8 to 16 bits. DNs can be turned back into
physical values by applying the reverse formula (x = (y - b) / a).
The GRASS GIS module i.landsat.toar easily transforms Landsat DN to
radiance-at-sensor. For other satellites, r.mapcalc can be employed.
Reflection/radiance-at-sensor and surface reflectance
When radiance-at-sensor has been obtained, still the atmosphere influences the
signal as recorded at the sensor. This atmospheric interaction with the sun
energy reflected back into space by ground/vegetation/soil needs to be
corrected. There are two ways to apply atmospheric correction for satellite
imagery. The simple way for Landsat is with i.landsat.toar, using the DOS
correction method. The more accurate way is using i.atcorr (which works for
many satellite sensors). The atmospherically corrected sensor data represent
surface reflectance, which ranges theoretically from 0% to 100%. Note that
this level of data correction is the proper level of correction to calculate
vegetation indices.
In GRASS GIS, image data are identical to raster data. However, a couple of
commands are explicitly dedicated to image processing. The geographic
boundaries of the raster/imagery file are described by the north, south, east,
and west fields. These values describe the lines which bound the map at its
edges. These lines do NOT pass through the center of the grid cells at the
edge of the map, but along the edge of the map itself.
As a general rule in GRASS:
- 1
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Raster/imagery output maps have their bounds and resolution equal to those
of the current region.
- 2
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Raster/imagery input maps are automatically cropped/padded and rescaled
(using nearest-neighbor resampling) to match the current region.
Imagery import¶
The module r.in.gdal offers a common interface for many different raster and
satellite image formats. Additionally, it also offers options such as
on-the-fly location creation or extension of the default region to match the
extent of the imported raster map. For special cases, other import modules are
available. Always the full map is imported. Imagery data can be group (e.g.
channel-wise) with i.group.
For importing scanned maps, the user will need to create a x,y-location, scan
the map in the desired resolution and save it into an appropriate raster
format (e.g. tiff, jpeg, png, pbm) and then use r.in.gdal to import it. Based
on reference points the scanned map can be rectified to obtain geocoded data.
Image processing operations¶
GRASS raster/imagery map processing is always performed in the current region
settings (see g.region), i.e. the current region extent and current raster
resolution is used. If the resolution differs from that of the input raster
map(s), on-the-fly resampling is performed (nearest neighbor resampling). If
this is not desired, the input map(s) has/have to be resampled beforehand with
one of the dedicated modules.
Geocoding of imagery data¶
GRASS is able to geocode raster and image data of various types:
- unreferenced scanned maps by defining four corner points (i.target,
i.rectify)
- unreferenced satellite data from optical and Radar sensors by defining a
certain number of ground control points (i.target, i.rectify)
- orthophoto based on DEM: i.ortho.photo
- digital handheld camera geocoding: modified procedure for
i.ortho.photo
Visualizing (true) color composites¶
To quickly combine the first three channels to a near natural color image, the
GRASS command d.rgb can be used or the graphical GIS manager (gis.m). It
assigns each channel to a color which is then mixed while displayed. With a
bit more work of tuning the grey scales of the channels, nearly perfect colors
can be achieved. Channel histograms can be shown with d.histogram.
Calculation of vegetation indices¶
An example for indices derived from multispectral data is the NDVI (normalized
difference vegetation index). To study the vegetation status with NDVI, the
Red and the Near Infrared channels (NIR) are taken as used as input for simple
map algebra in the GRASS command r.mapcalc (ndvi = 1.0 * (nir - red)/(nir +
red)). With r.colors an optimized "ndvi" color table can be assigned
afterward. Also other vegetation indices can be generated likewise.
Calibration of thermal channel¶
The encoded digital numbers of a thermal infrared channel can be transformed to
degree Celsius (or other temperature units) which represent the temperature of
the observed land surface. This requires a few algebraic steps with r.mapcalc
which are outlined in the literature to apply gain and bias values from the
image metadata.
Image classification¶
Single and multispectral data can be classified to user defined land use/land
cover classes. In case of a single channel, segmentation will be used. GRASS
supports the following methods:
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Radiometric classification:
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Unsupervised classification (i.cluster, i.maxlik) using the Maximum
Likelihood classification method
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Supervised classification (i.gensig or i.maxlik) using the Maximum
Likelihood classification method
Combined radiometric/geometric (segmentation based) supervised classification
(i.gensigset, i.smap)
Kappa statistic can be calculated to validate the results (r.kappa).
Image fusion¶
In case of using multispectral data, improvements of the resolution can be
gained by merging the panchromatic channel with color channels. GRASS provides
the HIS (i.rgb.his, i.his.rgb) and the Brovey transform (i.fusion.brovey)
methods.
Time series processing¶
GRASS also offers support for time series processing (<a
href="r.series.html">r.series). Statistics can be derived from a
set of coregistered input maps such as multitemporal satellite data. The
common univariate statistics and also linear regression can be calculated.
See also¶
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GRASS GIS Wiki page: Image processing
- The GRASS 4 Image Processing manual
- Introduction to GRASS 2D raster map processing
- Introduction to GRASS 3D raster map (voxel) processing
- Introduction to GRASS vector map processing
imagery index - full index
© 2008-2012 GRASS Development Team