DECORR

Description

Performs a decorrelation stretch (transformation) on 2 to 256 channels of image data. This type of stretch is especially effective on images in which channels are highly correlated (that is, the spectral bands are very similar). Decorrelation is based upon Principal Component Analysis and uses eigenchannels. Decorrelation stretching is a way of modifying, or stretching, the results of a PCA transformation.

Parameters

Name	Type	Length	Value range
Input: Input raster channels *	Raster port	2 - 256	-1024 -
Output: Output decorrelated channels *	Raster port	1 - 256	-1024 -
Mask: Area Mask (Window or Bitmap)	Bitmap port	0 - 4	Xoffset, Yoffset, Xsize, Ysize
Eigen Layers to Suppress	Float	0 - 16	1 -
Number of Eigen Layers to Save	Integer	0 - 16	1 -
Output Raster Type	Text	1 - 1024	8U, 16U, 16S, 32R Default: 16S
Report	String	0 - 192	See parameter description

* Required parameter

Parameter descriptions

Input: Input raster channels

Specifies the image channels to be decorrelated.

At least two channels must be specified, to a maximum of 256 channels. Duplicate channels are NOT allowed.

Output: Output decorrelated channels

Specifies the image channels to receive the decorrelated results.

The number of output channels must be the same or fewer than the specified input channels.

Each output channel saves the decorrelated results of the corresponding input channel. Duplicate channels are NOT allowed.

Mask: Area Mask (Window or Bitmap)

Specifies the bitmap that defines the area to be processed within the input raster.

If no value is specified, the entire channel is processed.

Regardless of the mask area defined, the entire image is decorrelated. The decorrelation is optimized over the mask area.

Eigen Layers to Suppress

Specifies the eigenchannels to suppress. Eigenchannels are numbered from 1 to the number of specified input channels.

Eigenchannels are often suppressed to reduce noise (error). Error is typically concentrated in the last or last few eigenchannels.

Eigenchannels are generated internally for the decorrelation process. The first eigenchannel contains the most variance, the second the next most variance, and the last the least.

At most, 16 eigenchannels can be suppressed.

Number of Eigen Layers to Save

Specifies the number of eigenchannels to save. Up to 16 eigenchannels can be saved.

Output Raster Type

Specifies the image data type of the output raster channel.

Available options are:

8U: 8-bit unsigned
16U: 16-bit unsigned
16S: 16-bit signed (default)
32R: 32-bit real

Report

Specifies where to direct the generated report.

Available options are:

LOG: generates a report on the terminal (default)
<modulename.RPT>: appends a report to the module's RPT file
DISK: generates a report on file "IMPRPT.LST"

Details

Decorrelation stretching is a method of enhancing multispectral image data, typically in an attempt to improve it visually. The basic concept is to force the variance between multispectral image channels to be maximized using a principal component transformation. The resulting imagery is (usually) quite similar to the original imagery, maintaining the average gray level and dynamic range, except that details have been improved, especially in areas which were 'uniform' in color (that is, correlated). Not all images, however, look better after decorrelation.

DECORR performs a decorrelation stretch on a set of input image channels (DBIC) in a PCIDSK image file. The results of the stretch are saved on the output channels (DBOC). Each output channel corresponds to the input channel.

More input channels can be specified than output channels. In these cases, the decorrelation transformation is calculated for all input channels, but only the specified output channels are saved. This is useful, because the extra channels usually contribute some information to the output channels, so the overall information content of the output channels is increased.

The decorrelation can be optimized for a specific region of interest (MASK). This region can be described by either a window (MASK=i,j,k,l) or a bitmap (MASK=i). If the MASK parameter is not specified, the entire image is used. Regardless of the interest region specified, the decorrelation is applied to the entire image.

It is possible to suppress the variance contribution of selected eigenchannels (EIGENSUP). This capability serves to improve image quality by suppressing noise (or error). When an eigenchannel is suppressed, its variance is suppressed, but its mean is still used. This ensures that the resulting decorrelated image has the same mean(s) as the original. Eigenchannels are a calculated, intermediate step in the decorrelation process. There is one eigenchannel for each input channel, and each one holds a decreasing amount of variance. Typically, the last few eigenchannels hold little variance, most of which is error, and these are the ones that are suppressed.

Algorithm

A decorrelation stretch attempts to maximize the variance between output channels. The basis for this is Principal Component Analysis, which is discussed in detail in the PCA documentation.

There are three steps performed in the decorrelation process:

Forward transformation: generates the principal component axes (eigenchannels) for the input image channels.
Scaling: the first principal component (eigenchannel) contains the most variance. The variances in the other components are scaled to match this first value. The result is that all components have the same maximum variance.
Backward transformation: the principal components, with scaled variances, are transformed back to the original image space and the user-specified channels are written back to disk. The output channels are similar in mean gray level and dynamic range to the original channels. The variance of each output channel is now greater than the original variance of the input channel; the variances of the output channels are approximately equal.

Suppression of eigenchannels (components) is done in step 2. Instead of scaling to match the variance of the first component, DECORR sets the scale factor to 0.

A visual interpretation of the effects of the decorrelation stretch using two channels is demonstrated in their scatterplots before and after decorrelation:


 C |                               C |                        
 h |                               h |             *          
 a |           *                   a |           *            
 n |          **                   n |        *  * *          
 n |        ***                    n |     ** *** *           
 e |        **                     e |      * * *  *           
 l |       ***                     l |     * * **              
   |      **                         |      **                 
 A |     *                         A |     *                    
   +------------------------         +------------------------
             channel B                         channel B

   Fig.1: Before decorrelation       Fig.2: After decorrelation

For further information on decorrelation stretching consult the following paper:

Gillespie, A.R., A.B. Kahle, R.E. Walker, 1986. "Color Enhancement of Highly Correlated Images. I. Decorrelation and HSI Contrast Stretches". Remote Sensing of Environment, Vol.20, p.209-235.

Using many channels

Although DECORR can handle up to 256 channels of data, using more than 32 is not recommended. The internal calculations are done in single precision (32-bit) floating point mathematics. Using a large number of channels tends to result in numerical instability and eventually poor results or program failure.

Output Report

The report below is an example of the report generated by DECORR.

irvine.pix                      [S  14PIC  512P   512L] 14-Aug-90

Input  Channels:   3   2   1   4   5
Output Channels:   7   8   9
Sampling Window:      0      0    512    512
Sampling Bitmap: none used
Sample size    :  32768


Input Channel       Mean       Deviation

     3            29.1240          9.5824
     2            25.4875          5.9492
     1            64.4771         10.0178
     4            39.7752         11.2962
     5            25.9260         11.1319

Covariance matrix for input channels:

              3           2           1           4           5
   +-----------------------------------------------------------
  3|     91.822
  2|     55.315      35.392
  1|     91.265      57.234     100.356
  4|     53.626      35.933      51.623     127.605
  5|     86.124      49.949      76.609      61.284     123.920

Eigenchan Eigenvalue  Deviation  %Variance  Scale Factor (Used)

     1      353.9080    18.8124     73.87%         1.00 ( 1.00)
     2       81.4424     9.0245     17.00%         2.08 ( 2.08)
     3       39.5849     6.2917      8.26%         2.99 ( 2.99)
     4        3.0105     1.7351      0.63%        10.84 (10.84)
     5        1.1499     1.0723      0.24%        17.54 (17.54)

Eigenvectors of covariance matrix (arranged by rows):

  0.48655427  0.29934525  0.48355350  0.41353855  0.51847798
  0.24679996  0.11529773  0.28296703 -0.90557152  0.16020662
  0.18398458  0.19743498  0.49801043  0.08589405 -0.81962007
  0.72211707  0.19005740 -0.63968229 -0.01107403 -0.18195906
 -0.38347274  0.90664017 -0.16999383 -0.03775945  0.02506943

Inverse eigenvector matrix (channels arranged by rows):

  0.48655421  0.24679999  0.18398456  0.72211707 -0.38347280
  0.29934525  0.11529772  0.19743498  0.19005737  0.90664023
  0.48355344  0.28296697  0.49801043 -0.63968223 -0.16999382

References