Change detection
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| + | == Prerequisites of multi-temporal image analysis == | ||
| + | # '''Acquisition of remote sensing data'''� by the same (or similar) sensor recorded using same spatial resolution, viewing geometry, spectral bands, time of day. | ||
| + | # '''Optical calibration�'''correcting uninteresting differences due to illumination, atmospheric conditions, topography, sensor calibration. DNs are converted to physical units (surface reflectance). | ||
| + | # '''Clear definitions and classification scheme''' | ||
| + | # '''Exact spatial coregistration''' | ||
== Change detection techniques == | == Change detection techniques == | ||
Revision as of 13:37, 1 February 2016
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This section is still under construction! This article was last modified on 02/1/2016. If you have comments please use the Discussion page or contribute to the article! |
Contents |
Prerequisites of multi-temporal image analysis
- Acquisition of remote sensing data� by the same (or similar) sensor recorded using same spatial resolution, viewing geometry, spectral bands, time of day.
- Optical calibration�correcting uninteresting differences due to illumination, atmospheric conditions, topography, sensor calibration. DNs are converted to physical units (surface reflectance).
- Clear definitions and classification scheme
- Exact spatial coregistration
Change detection techniques
Multi-temporal color composites
Principal component analysis
- Add the raster layers of the years 1992 (tm_920526_mul.tif), 2005 (etm_050623_mul.tif) and 2010 (etm_000515_mul.tif) into a QGIS project. It should be available in the course data.
- Install PCA plugin.
- Open PCA plugin.
- Set tm_920526_mul.tif as Input Raster File.
- Set Number of output Principal Components to 1.
- Save the Output Raster File as 1992_1.
- Repeat this procedure for the raster files of 2005 and 2010 and adapt the name of each Output Raster File.
- Merge the three output raster files with Toolbox --> GDAL/OGR --> [GDAL] Miscellaneous --> Merge
