Lab 4: Orthomosiac Creation Using ArcPro

Introduction

• What is photogrammetry?
• Photogrammetry is a 3 dimensional measuring technique that uses multiple photographs as the form of measurement. Points within each image are triangulated mathematically to produce a 3 dimensional coordinate system around specific points of interest.  

• What types of distortion does remotely sensed imagery have in its raw form?
• Remotely sensed imagery is subject to geometric distortion in its raw form, this is due to a number of reasons and varies depending on the data acquisition practices. Some of the factors that affect geometric distortion include, but are not limited to; the sensor angle, the platform altitude, the platform velocity, the platform stability, terrain changes and the curvature of the earth. These factors and distortions are visible in the raw data files and need to be corrected when applied to an orthomosiac in post processing, this will allow the user to create accurate data and images.   

• What is orthorectification? What does it accomplish?
• Orthorectification is the process of altering the raw images to adjust for camera tilt and terrain differences to ensure the images that are going into an orthomosiac are on the same geographic plane and are accurate. This allows for a more accurate orthomosiac with a consistent geographic scale throughout the image. Without this measurements within the image will not be accurate. 

• What is the Ortho Mapping Suite in ArcPro? How does it relate to UAS imagery?
• The Otho Mapping Suite in ArcPro allows users to take aerial imagery data from UAS platforms and create an accurate orthomosiac and map. The Ortho Mapping Suite will perform photogramical adjustments and orthorectification to the images to accurately create the data set and correct for geometric distortion in the raw data files. It allows the user to process an orthomosiac, DTM and DSM that are geographically consistent and accurate.   

• What is Bundle Block Adjustment?
• The bundle block adjustment is the last step in altering the data to provide more accuracy and correct for distortions encountered during data acquisition. The bundle block adjustment refines and alters the 3 dimensional geographic coordinates to accurately project the data.  

• What is the advantage of using this method? Is it perfect?
• It is another step in the adjustment of the data set to create more accurate final orthomosiacs and other models. It is not perfect but it allows the user to minimize distortions and create a more accurate data set. 

Method

When creating the orthomosiac we initially uploaded the raw image files from the flight to ArcGIS pro. In order to achieve the intended accuracy we performed a block adjustment within the data set. When the block adjustment was complete we were then able to process and accurate orthomosiac of the mission area. The processed orthomosiac was used in the creation of a map pictured in Figure 1. . The process of developing the orthomosiac is pictured in Figure 1.1. 

Figure 1.1: Orthomosiac Process via ArcGis.

• What key characteristics should go into folder and file naming conventions?
• When naming files it is important to take into account the type of file it is and the specific project that the file is related to. It would be helpful to create separate folders for each individual project and/ or flight data. Including specifics from the flight and project within the name will help organize the files and allow for easier navigation.

• Why is file management so key in working with UAS data?
• File management is important when working with UAS data due to the large number of files that one works with and the number of different file types that go into the data analysis of one project. UAS data can easily become confusing when working with so many files of the same type that have similar features. 

• What key forms of metadata should be associated with every UAS mission?

Figure 1.2: Metadata

Results

• Generate a table that shows the GCPs you used, and their coordinate locations.
• Pictured in Figure 1.3.

Figure 1.3: GCP's

• Describe you maps in detail. Discuss their quality, and where you see issues in the maps.
• The overall accuracy and quality of the map is fairly good, we can see noticeable changes in the accuracy before and after processing the block adjustment from Figure 1.4 to Figure 1.5. The major areas being inspected and pictured via the map are clear and accurate. From a visual standpoint the map looks clear and detailed, areas of interest are pictured well (Figure 1.6). Distortions have been corrected and we can use this map to take accurate measurements of the data set.   

• Are there areas on the map where the data quality is poor or missing?
• The major problem areas on the map are the areas with dense tree coverage or where there is building development. The distortions have not been fully corrected in the area with dense trees and we can see issues with the data processing as seen in Figure 1.6. Homes and other buildings within the data set cause for other difficulties as they also show some distortions in the data.    

• How much time did it take to process the data.
• Building image collection: 3 min 12 sec
• Block Adjustment: 1 hour 12 min
• Generate Orthomosiac: 16 min

Figure 1.4: Pre Block Adjustment

Figure 1.5: Post Block Adjustment

Figure 1.6: Orthomosiac Map

Conclusions

• Summarize the Orthomosaic Tool.
• The orthomosiac tool in ArcPro allowed us to create a fairly accurate map and provided data adjustments and corrections through photgramical corrections and orthorectification. There are obvious areas within the orthomosiac that are lacking quality such as the areas of dense trees. The success of this tool is dependent on the quality of the data that will be input into the program. It provides the user with a fairly quick and accurate form of data processing that will allow for accurate analysis. 

• Summarize the process in terms of time invested and quality of output.
• When creating the orthomosiac there were multiple issues encountered during the processing phase with regard to time. These issues could have been avoided by not uploading to our server or decreasing the amount of individuals processing at the same time. The total amount of time to process can be drastically reduced in a non-classroom environment. With this in mind the overall quality of the data is good and accurate for the time this should take to process. The longest time encountered during the process was the block adjustment with it clocking in at just over an hour. The block adjustment as the final correction for image distortion was expected to take a while. The size of the data set should also be take into account with processing time, if the data set was much larger the time would have drastically increased.  

• Think of what was discussed with this orthomosaic in terms of accuracy. How might a higher resolution DTM (from LiDAR) make this more accurate? Why might this approach not work in a dynamic environment such as a mine?
• A higher resolution model would be possible with the use of LiDAR data acquisition. Through LiDAR there will be drastically more data points that have been collected and therefore need to be processed. LiDAR has the ability to collect more data points through densely populated areas of foliage or infrastructure allowing for a very detailed data set and point cloud. This approach while being more accurate, drastically increases the processing time. The size of the data set will be much larger and take much longer in processing. This is not ideal in a dynamic environment where you need accurate data very quickly or almost on the spot.