Difference between revisions of "Image processing"

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== Introduction ==
 
== Introduction ==
  
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=== General introduction ===
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'''Digital numbers and physical values (reflection/radiance-at-sensor):'''
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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 at called "at-satellite", for example the amount of energy sensed by the sensor of the satellite platform is encoded in the 8 bits (or more). This energy is called radiance-at-sensor. To obtain the physical values from the DNs, satellite image providers use an affine transform equation (y=ax+b) to encode the radiance-at-sensor in 8/10/12/16bit. The user of satellite imagery has to apply the reverse formula to turn DNs back into physical values.
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In GRASS GIS, see the {{cmd|i.landsat.toar}} for an easy way to transform Landsat DN to radiance-at-sensor. If you are using ASTER data, use the {{cmd|i.aster.toar}} module. For other satellites, {{cmd|r.mapcalc}} can be employed.
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'''Reflection/radiance-at-sensor and surface reflectance'''
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Finally, once obtained radiance-at-sensor, still the atmosphere influences the signal between sensor and surface. This atmospheric interaction with the sun energy that the ground/vegetation/soil reflects back into space needs to be corrected. This can be done in two ways for Landsat (or other satellites). The simple way for Ladsat is with {{cmd|i.landsat.toar}}, using the DOS correction method. The more accurate way is using {{cmd|i.atcorr}} (which works for many satellite sensors). Once completed the use of an atmospheric correction on the energy sensed by the satellite, the corrected sensor data represent surface reflectance. Surface reflectance is theoretically ranging from 0.0 to 1.0.
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Note that this level of data correction is the proper level of correction to use with the vegetation index module {{cmd|i.vi|version=70}}.
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=== 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.
 
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.
  

Revision as of 03:40, 15 July 2013

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 at called "at-satellite", for example the amount of energy sensed by the sensor of the satellite platform is encoded in the 8 bits (or more). This energy is called radiance-at-sensor. To obtain the physical values from the DNs, satellite image providers use an affine transform equation (y=ax+b) to encode the radiance-at-sensor in 8/10/12/16bit. The user of satellite imagery has to apply the reverse formula to turn DNs back into physical values.

In GRASS GIS, see the i.landsat.toar for an easy way to transform Landsat DN to radiance-at-sensor. If you are using ASTER data, use the i.aster.toar module. For other satellites, r.mapcalc can be employed.

Reflection/radiance-at-sensor and surface reflectance

Finally, once obtained radiance-at-sensor, still the atmosphere influences the signal between sensor and surface. This atmospheric interaction with the sun energy that the ground/vegetation/soil reflects back into space needs to be corrected. This can be done in two ways for Landsat (or other satellites). The simple way for Ladsat is with i.landsat.toar, using the DOS correction method. The more accurate way is using i.atcorr (which works for many satellite sensors). Once completed the use of an atmospheric correction on the energy sensed by the satellite, the corrected sensor data represent surface reflectance. Surface reflectance is theoretically ranging from 0.0 to 1.0.

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.

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

Screenshots

Importing

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

Satellite Data

Ocean Color

Sea Surface Temperature (SST)

High resolution data

Orthophotos

Preprocessing

See also NASA LaRC Satellite Overpass Predictor

Geometric preprocessing/Georectification

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

Correction for atmospheric effects

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

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

Fourier Transform

Canonical Component Analysis

Principal Component 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

Time series analysis

Enhancements

Radiometric Enhancements

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

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

Geocoding ideas

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

Stereo ideas

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

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