Perfect Data Collection in Aerial Photogrammetry “8 Key Criteria”
In today’s digital age, the demand for high-quality aerial data is ever-increasing. Customers expect data that is accurate, reliable, and reasonably priced. Let’s delve into the tools and techniques that help both data producers and consumers achieve the best possible results.
What Defines Perfect Data in Aerial Photogrammetry?
The quest for perfect data is ongoing, but achieving 100% perfection is virtually impossible. However, our goal is to get as close as possible. Good data can be defined through several key criteria:
1) Completeness
We aim to collect, process, and deliver the most comprehensive datasets. However, various factors, such as weather, can impact data quality. For instance, clouds appearing in images can make datasets less usable.
To combat this, operators use real-time displays during flights to identify and mitigate issues immediately by adjusting the camera settings or even reflying the specific flight line while still in the air. In that way, full coverage of the AOI is guaranteed.
2) Data Reliability
Ensuring data integrity is crucial. During flights, data is redundantly stored, and consistency checks are performed during data transfer to rectify any discrepancies. This helps correct and reconstruct any faulty data. And finally, there are various options for outputting statistics and information about the data, such as reports, which can be used to document and verify the completeness of the data.
3) Accuracy
Achieving high accuracy is a major focus. Our calibration labs in our three worldwide service centers are equipped with numerous markers for geometric calibration, complemented by radiometric calibration. Post-laboratory calibration, verification flights with specific patterns are conducted to ensure accuracy.
4) Precision
Precision data involves consistently producing high-quality images. Our Adaptive Motion Compensation (AMC) technique addresses various forms of motion blur and scale variations, ensuring always sharp, blur-free images.
5) Correctness
Water surfaces can pose significant challenges in datasets, creating artifacts and distortions. Our innovative water handling approach involves automatically classifying water and non-water areas. This classification allows for targeted data processing, such as skipping water areas during DSM calculation to save computation time and avoid artifacts. Additionally, our UltraMap photogrammetry software estimates water height to ensure accurate and homogeneous orthomosaics, automatically improving data quality.
6) Visual Quality
We offer extensive options to adjust images as needed. Depending on the application, different settings can be applied. For example, a scene captured under diffuse light with a thin cloud cover on the left shows no shadows, while the same scene under strong sunlight on the right has significant visible shadows. This example demonstrates that despite our many adjustment capabilities, certain initial conditions may prevent perfect image matching.
7) Universal Usage
Different applications require tailored data processing. Whether calculating NDVI with data that is as raw as possible or performing the automated detection of bridges with the option to include or exclude them from the final DTM, our UltraMap workflow allows for flexible and efficient data handling.
8) Efficiency
Speed and cost-efficiency are vital. Our UltraCam system are tailored to different applications and our UltraMap software provides full flexibility to scale processing power and license modules as needed.
Conclusion
Achieving near-perfect data in photogrammetry involves meticulous attention to detail at every stage—from data collection and security to calibration and processing. By leveraging advanced techniques and flexible tools, we strive to meet the high expectations of our customers and deliver data of the highest possible quality.
At last, I would like to thank Bernhard Schachinger for his contributions.