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UltraMap 6.2: Efficient radiometric optimization with new archive features

Blog , Product Features
17 January 2024
New Radiometry Archive features in UltraMap 6.2
Many might wish it otherwise, but bad weather, air traffic control, or the size of the area often make it impossible to fly an entire project in just one day. This can lead to projects with strong radiometric fragmentation, which can negatively impact the overall global color balance. However, with the latest UltraMap 6.2 release and its innovative features for radiometric optimization, customers gain a powerful tool for addressing these challenges. The enhanced Radiometry Archives provide a convenient and flexible resource for handling data in these and many other scenarios.
New Radiometry Archive features in UltraMap 6.2
Many might wish it otherwise, but bad weather, air traffic control, or the size of the area often make it impossible to fly an entire project in just one day. This can lead to projects with strong radiometric fragmentation, which can negatively impact the overall global color balance. However, with the latest UltraMap 6.2 release and its innovative features for radiometric optimization, customers gain a powerful tool for addressing these challenges. The enhanced Radiometry Archives provide a convenient and flexible resource for handling data in these and many other scenarios.

Restore information using the Radiometry Archive


The Radiometry Archive allows you to export, store, and import radiometric data (parameters for every image, no image raster data) in a lightweight format. It serves as a pure storage format, where no data modification takes place. While it was already possible to import radiometric information for all images in a project, the new UltraMap Version 6.2 introduces the capability for partial imports, giving customers greater flexibility.

The new Radiometry Archive enables the export of radiometric settings for all images in a block, ensuring that all data are archived. During the import process, customers now can choose from multiple selections:

  • All images in a block
  • Specific images within a block
  • Images from an archive containing only a subset of the block’s images

Although this may appear to be a minor change, it significantly enhances the tool's versatility for various use cases. Let's explore how the new Radiometry Archive not only serves as a long-term data storage solution but also functions as a dynamic resource for ongoing projects.

Restore information using the Radiometry Archive


The Radiometry Archive allows you to export, store, and import radiometric data (parameters for every image, no image raster data) in a lightweight format. It serves as a pure storage format, where no data modification takes place. While it was already possible to import radiometric information for all images in a project, the new UltraMap Version 6.2 introduces the capability for partial imports, giving customers greater flexibility.

The new Radiometry Archive enables the export of radiometric settings for all images in a block, ensuring that all data are archived. During the import process, customers now can choose from multiple selections:

  • All images in a block
  • Specific images within a block
  • Images from an archive containing only a subset of the block’s images

Although this may appear to be a minor change, it significantly enhances the tool's versatility for various use cases. Let's explore how the new Radiometry Archive not only serves as a long-term data storage solution but also functions as a dynamic resource for ongoing projects.

Project Insights: Significant differences in vegetation


In this example, we see the input data from a project captured with the UltraCam Condor 4.1 over multiple flight days as part of the Vexcel Data Program, presented without any radiometric adjustments. The block overview displays significant differences in vegetation due to a collection period of about eight weeks in total.
Block overview without radiometric adjustments
After performing automated global color balancing using the standard approach in UltraMap, the block appears as a homogeneous project:
Block overview after color balancing

Most differences between the individual flight days are well compensated for. The color balancing result represents a compromise aimed at achieving the best overall solution, but it might be limited in preserving the details of local characteristics.

A zoomed-in view reveals the differences between two flight missions captured a few weeks apart. The left side was collected in June, when most agricultural fields were still green. The right side, captured in July, shows less greenery after the harvest of some fields.

Aerial image captured in two flight missions

Project Insights: Significant differences in vegetation


In this example, we see the input data from a project captured with the UltraCam Condor 4.1 over multiple flight days as part of the Vexcel Data Program, presented without any radiometric adjustments. The block overview displays significant differences in vegetation due to a collection period of about eight weeks in total.
Block overview without radiometric adjustments
After performing automated global color balancing using the standard approach in UltraMap, the block appears as a homogeneous project:
Block overview after color balancing

Most differences between the individual flight days are well compensated for. The color balancing result represents a compromise aimed at achieving the best overall solution, but it might be limited in preserving the details of local characteristics.

A zoomed-in view reveals the differences between two flight missions captured a few weeks apart. The left side was collected in June, when most agricultural fields were still green. The right side, captured in July, shows less greenery after the harvest of some fields.

Aerial image captured in two flight missions

Example 1: Vegetation or agricultural field


Techniques and impact of color balancing:

Color balancing aims to even out the radiometric differences in images. In this dataset, since most of the area consists of vegetation or agricultural fields, these elements dominate the color balancing process. They have more influence on the adjustment because they cover more pixels than other features, like roads.

Color balancing works effectively on the scale of entire images and even larger areas. As a result, whole images tend to shift towards the appearance of the dominating features. However, smaller features, like those only a few pixels in size, have to adapt to this shift. This effect becomes especially noticeable when individual ortho images are combined to form a mosaic.

In the example below, you can see this effect. The vegetated areas align well with each other, but the road in the center shows a noticeable change in its grey color. The seamlines of this ortho mosaic help to understand how the different images are pieced together.

The solution: Separate image subsets from the Radiometry Archive

Let’s re-process this scene, taking advantage of the new method for handling radiometric adjustment settings. Here are the steps at a glance:

  1. One Aerial Triangulation solution for all flight days within a single image block.
  2. Division of image blocks into subsets. In our case, the division was based on flight days. However, subsets can also be categorized based on image content, camera type, etc.
  3. Color Balancing for each image subset.
  4. Radiometric adjustment for each image subset.
  5. Export of image subsets to the Radiometry Archive.
  6. Import of all archives into the complete image block.

This approach allows for optimization tailored to the specific state of vegetation while maintaining consistent grey tones for the road surface. In this example, both blocks are well combined. The fields and vegetation appear natural, and the road maintains a consistent grey color. Due to changes in image radiometry, the seamlines are positioned slightly differently by the algorithm.

Example 1: Vegetation or agricultural field


Techniques and impact of color balancing:

Color balancing aims to even out the radiometric differences in images. In this dataset, since most of the area consists of vegetation or agricultural fields, these elements dominate the color balancing process. They have more influence on the adjustment because they cover more pixels than other features, like roads.

Color balancing works effectively on the scale of entire images and even larger areas. As a result, whole images tend to shift towards the appearance of the dominating features. However, smaller features, like those only a few pixels in size, have to adapt to this shift. This effect becomes especially noticeable when individual ortho images are combined to form a mosaic.

In the example below, you can see this effect. The vegetated areas align well with each other, but the road in the center shows a noticeable change in its grey color. The seamlines of this ortho mosaic help to understand how the different images are pieced together.

The solution: Separate image subsets from the Radiometry Archive

Let’s re-process this scene, taking advantage of the new method for handling radiometric adjustment settings. Here are the steps at a glance:

  1. One Aerial Triangulation solution for all flight days within a single image block.
  2. Division of image blocks into subsets. In our case, the division was based on flight days. However, subsets can also be categorized based on image content, camera type, etc.
  3. Color Balancing for each image subset.
  4. Radiometric adjustment for each image subset.
  5. Export of image subsets to the Radiometry Archive.
  6. Import of all archives into the complete image block.

This approach allows for optimization tailored to the specific state of vegetation while maintaining consistent grey tones for the road surface. In this example, both blocks are well combined. The fields and vegetation appear natural, and the road maintains a consistent grey color. Due to changes in image radiometry, the seamlines are positioned slightly differently by the algorithm.

Example 2: Suburban areas


Here's another example demonstrating the combination of imagery from two capture days in a suburban area. The image below presents the outcome of combined color balancing. This technique compensates, to a limited extent, for different atmospheric conditions like haze and shadows. However, the grey tones on the roads are still noticeably different. Once again, the seamlines are automatically positioned.
The new approach, which optimizes radiometric adjustment for each block, enables the creation of the ortho mosaic shown in the image below. Although it's not possible to completely eliminate the varying appearances caused by building lean, such as sunlit facades versus shadowy areas, this method aligns other image characteristics effectively. This includes aligning the grey tones of roads, which serve as a reference.

Example 2: Suburban areas


Here's another example demonstrating the combination of imagery from two capture days in a suburban area. The image below presents the outcome of combined color balancing. This technique compensates, to a limited extent, for different atmospheric conditions like haze and shadows. However, the grey tones on the roads are still noticeably different. Once again, the seamlines are automatically positioned.
The new approach, which optimizes radiometric adjustment for each block, enables the creation of the ortho mosaic shown in the image below. Although it's not possible to completely eliminate the varying appearances caused by building lean, such as sunlit facades versus shadowy areas, this method aligns other image characteristics effectively. This includes aligning the grey tones of roads, which serve as a reference.
Radiometric Excellence: Unlocking the potential of radiometry archives

Our examples show how radiometrically optimizing data from each individual flight mission allows for the most effective management of local characteristics. With UltraMap 6.2, customers gain efficiency as they can now handle local radiometric adjustments at the block level, rather than dealing with each image individually. Moreover, differences between individual images become more apparent within the block radiometry, where all images are combined into an orthomosaic. The process ensures that continuous features like roads are easier to homogenize for consistent appearance across all data segments than before. Furthermore, discrepancies in other features are handled by strategically placing seamlines in the ortho mosaic.
Radiometric Excellence: Unlocking the Potential of Radiometry Archives

Our examples show how radiometrically optimizing data from each individual flight mission allows for the most effective management of local characteristics. With UltraMap 6.2, customers gain efficiency as they can now handle local radiometric adjustments at the block level, rather than dealing with each image individually. Moreover, differences between individual images become more apparent within the block radiometry, where all images are combined into an orthomosaic. The process ensures that continuous features like roads are easier to homogenize for consistent appearance across all data segments than before. Furthermore, discrepancies in other features are handled by strategically placing seamlines in the ortho mosaic.
Interested in discovering more about how UltraMap enhances image quality? Dive deeper into the world of True Pixel Processing.
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