Tips for using color correction on raster datasets

Color correction is a new feature that is offered at ArcGIS 9.3. We’ve been getting some questions about what it is and how to use it, so I talked to the raster team and they helped put this post together.

Color correction can be applied during a mosaicking process or to an existing raster catalog. It is used to make the catalog or mosaic appear seamless.

Color correction consists of color balancing and color matching options:

• Color balancing aggregates the statistics from all pixels, and then balances out the differences in each image.

• Color matching uses a reference raster dataset to match against the source raster datasets. Overlapping areas in the reference and source datasets are compared and then an algorithm is applied to match colors in the source datasets.

                                                         
There are two places that you can access the full set of color correction options:

• In ArcMap, the color correction tab can be used in the raster layer properties dialog 
• Through geoprocessing the Raster Catalog to Raster Dataset tool can be used. 

In addition, there are two geoprocessing tools that allow for color matching options:

• Mosaic tool
• Workspace to Raster Dataset tool

Here are a few tips that you should be aware of to help you get better results when performing color correction:

• Data with a lot of overlap is required for Color Matching. These overlapping areas should have a representative sample of features and spectral values. Color balancing can be performed with no overlapping areas.

• Selecting the reference raster manually is a good idea. This way you can choose the best reference for the algorithm. A good reference raster will have a lot of overlap with other datasets, and will have a range of values that is representative of the entire catalog. 

• It is always a good idea to prototype your color correction parameters in ArcMap on-the-fly, so that you can see what your output would look like. The Mosaic Raster Catalog function can then mosaic your catalog into a dataset – persisting the color correction parameters that you have chosen.

• If you do not like the output that you have prototyped, try a different method or a different reference raster. 

• Choosing an appropriate mosaic method and raster catalog order might help cover up areas that are not displaying well.

• The data is key to color correction. Anomalies in the datasets, such as clouds or different tones of water, will result in poor color correction outputs.

Here is an example workflow for color correcting a raster catalog in ArcMap.

When the raster catalog is initially rendered, we can see that some of the tiles look different.  In particular, the bottom of the catalog is lighter in color than the top.

Raster catalog layers now have a new color correction tab within the layer properties dialog. This tab allows you to color balance, or color match your raster catalog items.

The following screen capture shows the results of color balancing on the raster catalog. As we can see, the catalog now looks a lot more seamless.

The results of color matching with the default method (Statistics Matching) are displayed in the image below. Once again it is a fairly good match, but there are still some areas that might show the seams between the catalog items.

Using various other matching methods can produce different results. The three methods are Statistics Matching, Histogram Matching, and Linear Correlation.

• Statistics Matching will match the statistical similarities (using minimum, maximum, and mean) between the reference overlap area and the source overlap area, and then apply the color transformation to the source datasets. 

• Histogram Matching will match the histogram from the reference overlap area with the source overlap area, and then apply the color transformation to the source datasets. 

• Linear Correlation will match the overlapped pixels and interpolates the rest of the source. The pixels that do not have a one-to-one relationship will use a weighted average. 

The result from each method will rely on how much overlap there is, and how well the overlap areas represent the non-overlap areas of the image.

Below is the histogram matching method (with the default reference raster).

Changing which raster dataset is used as the reference raster will also change your results. Below is the histogram matching method, but with a different reference raster dataset.

Something to keep in mind, color correction is a mathematical algorithm that is applied to the adjacent raster datasets. The results of color correction can only be as good as the input data – especially when color matching. If each of the datasets is quite different from each other (e.g. captured with difference sensors or time of day) then the algorithm used will be of limited use. As a rule of thumb, the best results from color correction are gained from images captured under similar conditions.