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Summary

RADAR Processing for use with with single band RADAR data.

LicenseLGPL
PriceFree
AuthorNathan Jennings
Website

www.jenningsplanet.com

Latest Version1.3
Updated On2010-07-11
Download

Download and drag/drop or Opticks Help/Extensions/Install to install the latest version of the RADAR Processing plugin (RADARProcessing.AEB).  Use the sample Radarsat image if desired.

Summary

This extension computes three speckle reducing filters and three texture measures often used with RADAR data.

Supported Opticks Releases: 

4.11.X
4.10.X
4.9.X
4.8.X
4.7.X
4.6.X
4.5.X
4.4.X
4.3.X
4.2.X

Supported Operating Systems: 

Windows 32-bit
Windows 64-bit
Solaris SPARC 64-bit
Linux 64-bit

 

Speckle Reduction

1. Mean

2. Median

3. Regional

Texture Analysis

1. Skewness
2. Kurtosis
3. Variance

The speckle filters reduce the "speckle effect" typically found in RADAR data.  Currently, this extension computes 3 common measures.  Others will be added in future releases.

The texture measures quantify the "variability" of pixel data within a given "moving window."

The current version only uses a 3x3 window. Future updates will expand this capability to accommodate other window sizes.

See attached AEB, latest version of the source code, documentation, and sample imagery.  Only the AEB is required to run the texture algorithms within Opticks.

Version 1.3 (11.07.2010)

Created AEB for easy install (drag/drop or Opticks Help/Extensions/Install).  Additional Help documented auto loaded into the Opticks Help menu for RADAR Processing on-line help.

Version 1.2 (06.10.2010)

Modified the menu systems (from RADAR to RADAR Processing) and have added 2 submenus (Texture Analysis and Speckle Reducing Filters).  The speckle reduction filters have been added to this version.

Version 1.1 (05.30.2010)

Implements a generic moving window to process the algorithms.  This eliminates the "hard coded" code in version 1.0.  The generic moving window will allow for future enhancements using a variable window size.  This version also implements an offset structure on the input image so that the "edge" rows/columns of the input image are not processed.  For example, a 3x3 window will process the input image that are offset by 1 row and 1 column.  For a 5x5 window, this offset would be 2 rows and 2 columns.  The output image will show an edge of 0s equal to the offset.

 Version 1.0 (05.26.2010)

Implemented the "hard coded" access to a "sub window."  This implementation makes it difficult to implement other "window sizes."

Texture Analysis

Skewness

Computes Skewness Statistical Measure how "shifted" a distribution curve is.
A large positive skewness indicates a "shift" to the right of the mean.  A large negative value indicates a shift to the
left of the mean.

From an image processing "texture" point of view large (positive or negative) skewness values indicates a trend in the
local neighborhood of brightness values.  A broad area of negative values can indicate that the original brightness values
tend to be small, but similar.  A broad area of positive values can indicate that the original brightness values
tend to be larger, but similar.  The resulting skewness image can help in determining some land cover types or assit
to "segment" the image into areas with distinct texture patterns.

Kurtosis

Computes Kurtosis Statistical Measure how "peaked" or "flat" a distribution curve is.
A high peak about the mean that falls off rapidly and has "heavy" tails indicate high kurtosis.

From an image processing "texture" point of view large kurtosis values indicates that many pixel values are near the mean
within a local neighborhood (e.g. 3x3, 5x5, 7x7 area) and a smaller number of pixels are further away from the mean
(i.e. are towards the tails of the distribution curve).  One can say that most of the pixels in a local area
are "similar" to one another, but do have some pixels that are very different.  The resulting kurtosis image
can help in determining some land cover types or assit to "segment" the image into areas with distinct texture
patterns.

Variance

Computes Variance Statistical Measure how "variable" a distribution curve is.
A large variance indicates that pixels within a local neighborhood are different from one another.  A small variance
indicate that pixels are similar to one another and may have a normal distribution.

The resulting variance image can help in determining some land cover types or assist to "segment" the image into areas with distinct texture patterns.

Speckle Reduction Filters

Mean

Computes the men of the moving window and writes the value to the output image. The mean calculation is also used in all of the current texture measures.

Median

Computes the median (middle value) of a moving window.  This algorithm fills an array and is then sorted to determine the median.

Regional

Computes a regional homogeneity value of small "subsections" within the moving window.  The section with the lowest variability is written to the output image.


All Downloads

RADAR Processing Extension, Imagery, Source Code