Image processing

Introduction

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. The equivalent module for ASTER data is i.aster.toar. 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.

Image processing in GRASS GIS

Satellite imagery and orthophotos (aerial photographs) are handled in GRASS as raster maps and specialized tasks are performed using the imagery (i.*) modules. All general operations are handled by the raster modules.

• imageryintro: A short introduction to image processing in GRASS 6
• Full GRASS 4.0 Image Processing manual (PDF, 47 pages)
• imagery: Imagery module help pages

• Data import is generally handled by the r.in.gdal module

Screenshots

• The imagery screenshots page

Importing

The wxGUI offers a convenient tool for single map and bulk import:

• see Importing data

Satellite Data

Ocean Color

• MODIS
• SeaWiFS

Sea Surface Temperature (SST)

• MODIS
• AVHRR

High Resolution Data

• IKONOS
• QuickBird
• WorldView

See also, various Whitepapers on High Resolution Satellite Imagery

Orthophotos

• see Orthorectification
• see Ortho-rectification of oblique photographs

Preprocessing

See also NASA LaRC Satellite Overpass Predictor

Geometric preprocessing/Georectification

• Georectification tool is available from the File menu in the GUI.
• i.points, i.vpoints (scanned maps, satellite images)
• i.ortho.photo (aerial images)

A multi-band image may be grouped and georectified with a single set of ground control points (i.group, i.target, i.rectify).

See also the Georeferencing wiki page

Radiometric preprocessing

• use r.mapcalc to apply gain/bias formula
• LANDSAT: you can also use i.landsat.toar from GRASS AddOns (included since 6.4)

Correction for atmospheric effects

Visit the dedicated page on Atmospheric correction

Related Modules

• i.landsat.dehaze: simple dark-object/Tasseled Cap based haze minimization (from GRASS AddOns)
• i.atcorr: more complex correction but based on atmospheric models

Correction for topographic/terrain effects

In rugged terrain, such correction might be useful to minimize negative effects.

• simple "cosine correction" using r.sunmask, r.mapcalc (tends to overshoot when slopes are high)
• In i.topo.corr the following correction methods are implemented: cosine, minnaert, percent, c-factor.
  • Note, that for the sun's zenith (in degrees) parameter, the equation "Sun's Zenith = 90 - Sun's Elevation" is generally valid

Cloud removal

• with i.landsat.acca

Image classification

See the dedicated Image classification page.

Image segmentation

• i.smap: Performs contextual image classification using sequential maximum a posteriori (SMAP) estimation.
• r.seg: Performs image segmentation and discontinuity detection (based on the Mumford-Shah variational model).
• i.segment: Image Segmentation

Filtering

• r.mfilter, r.neighbors

Fourier Transform

• i.fft, i.ifft
• see also Image destriping
• see also Fourier transforms for multitemporal analysis

Canonical Component Analysis

• i.cca

Principal Component Analysis

• i.pca
• see also Principal Components Analysis

Texture

A series of commonly used texture measures (derived from the Grey Level Co-occurrence Matrix, GLCM), also called Haralick's texture features are available:

• r.texture: In case of panchromatic maps or limited amount of channels, it is often recommended to generate synthetic channels through texture analysis

See here and here for the formulas to calculate texture. See also canopy texture mapping.

Spectral unmixing

• i.spec.unmix is used to perform Spectral Unmixing (manual)
• i.spec.unmix (src): version for GRASS GIS 7

Thermal remote sensing

• r.mapcalc can be used to convert from DN (digital number) of arbitrary sensors to Kelvin/Celsius/...
• i.landsat.toar - Calculates top-of-atmosphere radiance or reflectance and temperature for Landsat MSS/TM/ETM+/OLI
• i.aster.toar - Calculates top-of-atmosphere radiance or reflectance and temperature for ASTER
• MODIS

Time series analysis

• r.series- Makes each output cell value a function of the values assigned to the corresponding cells in the input raster map layers.
• see also Time series
• see also Time series development
• see also MODIS

Enhancements

Radiometric Enhancements

• i.landsat.rgb
• Decorrelation stretching with r.colors or r.mapcalc
• Density slicing with r.colors
• Principal Component Analysis with i.pca

Geometric Enhancements - Image Fusion - Pansharpening - Image Segmentation

Image fusion and Pansharpening:

• i.rgb.his and i.his.rgb: can be used for image fusion
• i.fusion.brovey: image fusion of pan-chromatic and color channels
• i.pansharpen: Image fusion algorithms to sharpen multispectral with high-res panchromatic channels (GRASS 7)

Segmentation:

• r.seg which performs image segmentation and discontinuity detection (based on the Mumford-Shah variational model). The module generates a piece-wise smooth approximation of the input raster map and a raster map of the discontinuities of the output approximation. The discontinuities of the output approximation are preserved from being smoothed. (Addons)
• i.segment: Identifies segments (objects) from imagery data (GRASS 7)

Optimal channel selection for color composites

• i.oif

Vegetation indices

• r.mapcalc can be used to calculate vegetation indices
• In GRASS GIS 7, see also i.vi
• Search this wiki for NDVI (see top right search field)

Evapotranspiration

• In GRASS GIS 7, i.eb.* and i.evapo.* are modules dedicated to evapotranspiration

Stereo anaglyphs

• see Stereo anaglyphs

Ideas collection for improving GRASS' Image processing capabilities

Below modules need some tuning before being added to GRASS 6. Volunteers welcome.

Spectral unmixing ideas

• Make use of the Spectral Python (SPy) which is a pure Python module for processing hyperspectral image data

Spectral angle mapping ideas

• i.spec.sam

Geocoding ideas

• i.homography: geocoding with lines (instead of points) with homography (as improved i.points; it was formerly called i.linespoints)
• support splines from GDAL (see GRASS_AddOns#Imagery_add-ons)
• New Georectifier: see also http://gama.fsv.cvut.cz/~landa/grass/swf/georect.html

Image matching ideas

• i.points.auto: automated search of GCPs based on FFT correlation (as improved i.points)

Reference: M. Neteler, D. Grasso, I. Michelazzi, L. Miori, S. Merler, and C. Furlanello, 2005: An integrated toolbox for image registration, fusion and classification. International Journal of Geoinformatics, 1(1), pp. 51-61 PDF

Image classification ideas

• pr: C code for classification problems
• GRASS implementation: i.pr.* source code is available here)

Stereo ideas

This is stand-alone stereo modeling software (DEM extraction etc). Waits for integration into GRASS.

• http://grass.osgeo.org/outgoing/grass5/stereo-0.2b.tar.gz
• Stereo Tutorial

Bundle block adjustment

Needed to orthorectify a series aerial images taken sequentially with overlap. "Histoical" method which is nowadays interesting for UAV flights with octocopters and such.

Automatec GPC search could be done by "auto-sift".

Available: Octave code which prepares input to an i.ortho.photo batch job (contact Markus Neteler).

Lidar LAS format

LAS Tools by M. Isenburg, Howard Butler et al.: http://www.liblas.org

   las2txt | r.in.xyz in=- fs=" "

Update: r.in.lidar and v.in.lidar implemented by Markus Metz (GRASS 7)

(see LIDAR)

Improving the existing code

It might be sensible to merge the various image libraries:

• GRASS 6 standard libs:
  • lib/imagery/: standard lib, in use (i.* except for i.points3, i.rectify3, see below)
  • imagery/i.ortho.photo/libes/: standard lib, in use (i.ortho.photo, photo.*)
• GRASS 5 (! only) image3 lib:
  • libes/image3/: never finished improvement which integrated the standard lib and the ortho lib. Seems to provide also ortho rectification for satellite data (i.points3, i.rectify3)
• GRASS 5/6 image proc commands:
  • merge of i.points, i.vpoints, i.points3 (see above)
  • merge of i.rectify and i.rectify3 (see above)
  • addition of new resampling algorithms such as bilinear, cubic convolution (take from r.proj or r.resamp.aggreg) (done 10/2010)
  • add other warping methods (maybe lanczos or thin splines from GDAL?): Addons#i.warp
  • implement/finish linewise ortho-rectification of satellite data

Bibliography

• Search for "GRASS GIS Image processing" - Google Scholar
• Search for "GRASS GIS Remote Sensing" - Google Scholar