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Description:

 

 Due to binning of statistics (and other attribute tables), copying the contrast table from one 16-bit image (such as DigitalGlobe's WorldView imagery or ESA's Sentinel-2) to another is not straightforward. This model simplifies that process.

 

Why should I be interested in transferring Contrast Tables using a Spatial Model? The basic issue is that if you are trying to visually compare two (or more) images, whether they be of the same location but different dates, or of neighboring locations, the differing statistical distributions in each image will result in differing display characteristics when default statistical stretches are applied. For example, these two DigitalGlobe WorldView-2 images (8-band, unsigned 16-bit, displayed using a 8,6,1 band combination), were displayed using the default 2.5% / 1% Percentage stretches:

 

 Two WorldView-2 images displayed side-by-side in a single 2D View
Stretch_Percentage.png

 

The image on the right has a large percentage of its pixels in the ocean and so has a markedly different histogram from that of the predominantly land-based image on the left. Consequently the "same" statistical stretch applied to the two images results in markedly different contrast tables and markedly different visual appearance. 

 

What we need to do is literally use the same Contrast Table to display both images. I.e if DN value 389 in the left image is being mapped to screen brightness 125 we want the same mapping to be applied to the image on the right image.

 

So first we can manipulate the radiometry of the left hand image (which we will now refer to as the From image). Alter the display parameters to get the appearance you want to be used for all other images. For the purposes of this demonstration I applied a 2 SD stretch and then clicked the Save icon on the Quick Access Toolbar so that the stretch parameters were saved to the Contrast Tables of the From image (for just the displayed bands 8,6 and 1 since only those bands were displayed).

 

Left (From) image has had 2 SD stretch applied
Stretch_SD.png

 

This saved Contrast Table is what we want to transfer to the right image (which we will now refer to as the To image).

 

For 8-bit (or lower) imagery this is a relatively easy task. You Can Save Breakpoints to a file from the From image and then Load Breakpoints for the To image and you're done. But even for 8-bit data this is a manual process that has to be performed using the GUI, whereas you may want to automate the process using a Spatial Model (and the Batch tool). But more importantly, with imagery using greater then 8-bit pixel depths (such as this u16 WorldView-2 imagery) the statistics (and other attribute information associated with the DN values) are binned (usually using Direct Binning). One consequence of Direct Binning is that if, for example, a band has no pixels with DN values from 0 to 39, the attribute information starts at value 40 (which is then considered Bin 0). Whereas a second image may not have values between 0 and 79 and so it's binned attributes start at 80 (as Bin 0). This makes transferring a binned Contrast Table from one 16-bit image to another tricky because the relative offsets of the start of the table need to taken into account.

 

For example, here's the (start of) the Contrast Table for a u16 image whose Minimum DN value  is 318. In other words, Bin[0] corresponds to DN value 318 in this image. Bin[1] is DN 319. Etc.

 

 HfaView showing the Contrast Table saved in an image header
Contrast.png

 

The model transfer_lut_v16_1_8.gmdx takes care of these relative offsets of the start of the Table for you.

 

transfer_lut_v16_1_8.gmdx
Transfer_LUT.PNG

 

Remove both images from the 2D View, run the spatial model transfer_lut_v16_1_8.gmdx and then re-display both images and the result looks like this (because a common contrast table is now being used to stretch both images):

 

 After running transfer_lut_v16_1_8.gmdx and redisplaying both images
Stretch_Transfered.png

 

With this example the two images now appear seamless (because they are actually tiles from the same data collect). The images you apply this technique to may still appear different after transferring a common contrast table between them. But you will now be sure that the visual difference you are seeing is truly because of differences in DN values, nor an arbitrary difference imposed by using different stretches. This makes visual change detection, for example, a much more objective analysis technique.

 

Assumptions

 

  1. IMPORTANT: All bands of both From and To images should have Statistics calculated on them prior to creating the contrast table for the From image. For IMG format files (or for other format files created using ERDAS IMAGINE)  this is almost always already the case. However for NITF, TIFF and other non-IMG formats created in packages other than ERDAS IMAGINE, statistics may be absent. QuickStats (created by opening such an image in the 2D View) are not sufficient for this model to correctly transfer the Contrast table between images. If in doubt, use the Image Info utility (accessed from the Home tab, Information group, Metadata pulldown and selecting the View/Edit Image Metadata option) to review the image. If any bands have a Skip Factor of 999 (indicating QuickStats) or if Statistics are absent (Statistics Info is greyed out) then full statistics should be calculated before proceeding. 

    Statistics can be calculated in either the Image Info utility mentioned, or, if you wish to calculate statistics on a large number of images at once, use the Edit Image Metadata option (from the Home tab, Information group, Metadata pulldown ) in combination with the Batch tool. A Skip Factor (other than 999) can be used to speed up the process, but be aware that using a skip greater than 1 will result in statistics that aren't necessarily accurate. But they should be fine for the purposes of transferring a Contrast table. 

    Once statistics have been calculated you can then go ahead and display the From image in a 2D View in order to create and save the contrast table you wish to use.

  2. Both images must have the same data type (e.g. both must be u16) and both must have the same number of bands (e.g. both must have 4 bands).  When the contrast is transferred it is transferred between "like" bands. For example, the contrast table of "From image (band 2)" would be transferred to "To image (band 2)".
 
Model Functions and/or Operations

 

How does the model work?

 

The main model shown above, outside of the Iterator, first looks at the input From image to determine if the bit depth is 8 or less. If it is, then the subsequent operations within the Iterator are much simpler since the From contrast table(s) can be simply copied into the To contrast table(s) without modification.

 

For data greater than 8-bit further analyses are performed on the input From and To images to determine the binning differences between the two. The Statistics of each image are interrogated, primarily in order to determine if the binned contrast table(s) of the From image start at a DN value greater than, or less than that of the corresponding binned contrast table(s) of the To image. A From contrast table that started later would need to be padded on the front, whereas one that started earlier would need to be trimmed to match that of the To attribute tables.

 

The size of the trimming or padding necessary is also determined by looking at the differences (ranges) between Mins and between the Maxs.

 

The Metadata of the input To image is also interrogated and a Dictionary Item operator is used to read the BandName(s) of the input To layers. 

 

All of the results at this stage, bar the "is it 8-bit or less" boolean, produce Tables with n rows, where n is the number of bands in the input images. For example, if the input image each have 8 bands, Tables with 8 rows (and appropriate values) will be created. Since all the Tables being input to the Iterator operator will have the same number of rows, the Iterator will run n times, each time using the nth row value from every table as the inputs to that iteration.

 

Inside the Iterator it gets a bit more complicated.

 

Iterator sub-model
Transfer_Iterator.PNG

 

The Iterator sub-model is iterating on each band based on the input values for that band. The Contrast Attribute Table is read from band n of the From image. Based on the relative positioning and size of that Contrast table compared to the To image's Attribute Tables, there are various If Else branches which control whether it is padded or trimmed, how much it is padded or trimmed and what values are used for any padding. There are also two If Else operators which look to see if the particular From image band being processed has no Contrast Table and attempts no transfer in that instance. Otherwise the adjusted Contrast Table is attached to the To image using the Raster Attribute Output operator. This process is repeated until all bands of the input image have been processed. 

 

When padding of the Contrast Table is required, the Append Table sub model is used to stack two Tables (current Contrast and the Padding table) together. The operation of this is described in the Table Stack Snippet article.

 

When padding the end of the Contrast Table, the Get Last Value in Table sub-model is used to determine the last value present in the Contrast Table (it might not be 1.0!) and a padding Table is created populated with that value (and with the number of rows necessary to pad the Contrast Table the appropriate amount).

 

Get Last Value in Table sub-model
Get Last Value.PNG

 

 

 

Input parameters:

 

From: Name of an input image file containing saved Contrast Table(s) to be transferred to another image. The image can be 1 band or more.

To: Name of an existing image to which the Contrast Table(s) will be attached. The To image must have the same number of bands, and the same bit depth, as the From image.

 

 

Transfer_GUI.png

   

 

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