i.smap: Performs contextual image classification using sequential maximum a posteriori (SMAP) estimation.See the dedicated Image classification page. Topographic correction of Landsat imagery using GRASS GIS (Blog article).Note, that for the sun's zenith (in degrees) parameter, the equation " Sun's Zenith = 90 - Sun's Elevation" is generally valid.In i.rr the following correction methods are implemented: cosine, minnaert, percent, c-factor.simple "cosine correction" using r.sunmask, r.mapcalc (tends to overshoot when slopes are high).In rugged terrain, such correction might be useful to minimize negative effects. i.atcorr: more complex correction but based on atmospheric modelsĬorrection for topographic/terrain effects.i.haze: simple dark-object/Tasseled Cap based haze minimization (from GRASS AddOns).Visit the dedicated page on Atmospheric correction Related Modules LANDSAT: you can also use i.landsat.toar from GRASS AddOns (included since 6.4).use r.mapcalc to apply gain/bias formula.see Ortho-rectification of oblique photographs.i.points, i.vpoints (scanned maps, satellite images)Ī multi-band image may be grouped and georectified with a single set of ground control points ( i.group, i.target, i.rectify).Georectification tool is available from the File menu in the GUI.See also NASA LaRC Satellite Overpass Predictor Geometric preprocessing/Georectification various Whitepapers on High Resolution Satellite ImageryĪ multi-band image may be grouped with i.group (some commands actually require the input being defined as a imagery group rather than a list of map names).Spectral Response specifications for IKONOS, GeoEye, QuickBird, WorldView.The wxGUI offers a convenient tool for single map and bulk import: Data import is generally handled by the r.in.gdal module.Full GRASS 4.0 Image Processing manual (PDF, 47 pages).imageryintro: A short introduction to image processing in GRASS 6. All general operations are handled by the raster modules. Satellite imagery and orthophotos (aerial photographs) are handled in GRASS as raster maps and specialized tasks are performed using the imagery (i.*) modules. Note that this level of data correction is the proper level of correction to calculate vegetation indices. The atmospherically corrected sensor data represent surface reflectance, which ranges theoretically from 0 % to 100 %. The more accurate way is using i.atcorr (which works for many satellite sensors). The simple way for Landsat is with i.landsat.toar, using the DOS correction method. There are two ways to apply atmospheric correction for satellite imagery. This atmospheric interaction with the sun energy reflected back into space by ground/vegetation/soil needs to be corrected. When radiance-at-sensor has been obtained, still the atmosphere influences the signal as recorded at the sensor. Reflection/radiance-at-sensor and surface reflectance For other satellites, r.mapcalc can be employed. The equivalent module for ASTER data is i.aster.toar. The GRASS GIS module i.landsat.toar easily transforms Landsat DN to radiance-at-sensor. DNs can be turned back into physical values by applying the reverse formula (x = (y - b) / a). 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. This energy is called radiance-at-sensor. 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. Having data stored in DN, it implies that these data are not yet the observed ground reality. 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. the originally sampled analog physical value (color, temperature, etc) is stored a discrete representation in 8-16 bits. Satellite imagery is commonly stored in Digital Numbers (DN) for minimizing the storage volume, i.e.
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