PCI Geomatics - Version HAP -Historical Airphoto Processing System
Historical aerial photography archives contain valuable information that remains untapped. Digitally scanned and properly geo-referenced historical aerial imagery can bring this information to life, making it possible to analyze/visualize the historical information in modern GIS systems. These historical images can reveal hidden patterns, provide a deeper understanding of changes over time thus leading to better decision making.
Tap Into Your Valuable Historical Imagery
Start realizing the value of your vast archives of historical airphoto imagery today and turn hundreds of thousands of archive images into GIS ready digital mosaics.
The HAP Method
PCI’s Semi-automated method to process large volumes of corrected historical airphotos includes:
- Automated Fiducial Mark collection
- Automated GCP / TP collection methods
- Large volume block bundle adjustment
- Automated mosaicking and color balancing
- Quality Assurance tools
Data Prep
Data preparation is required prior to running the scripts in this workflow. Data preparation ensures that the input imagery, reference imagery, input metadata and folder structure adhere to the conventions required by the scripts.
Data Ingest
Data ingest is semi-automated; including manual steps for checking the quality fiducials and exterior orientations. In most cases, fiducials are collected on one image, then automation will collect fiducials based on pattern recognition techniques.
Quality Check Fiducials and Exterior Orientation
Confirming the quality of fiducials and exterior orientation information is important to help prevent errors from occurring during processing. This becomes even more valuable when the volume of projects require hours of processing time.
Coarse Alignment
An automated script automatically collects and refines GCPs and Tie Points for all input images in the project. The script also computes a bundle adjustment and provides statistics to help ensure that the points collected will improve the alignment. The output generated by this coarse alignment is then used as an input into the next iteration, which produces highly accurate results.
Quality Check GCPs and TPs
Manually validate the number and spatial distribution of GCPs/TPs collected for each image.
Fine Alignment
A second automated script automatically collects and refines GCPs/TPs for all input images in the project. The purpose of this adjustment is to perform a final update to the math model based on the new GCPs and TPs collected. The result of this adjustment should provide a math model that will generate highly accurate orthos.
Orthorectification
The user can generate orthorectified images using PCI`s ORTHO2 algorithm or GUI interface application, OrthoEngine.