Starting Out Understanding Image Overlap
- May 24, 2016
- Blog, Uncategorized
- Posted by Felix.Weber
- Comments Off on Starting Out Understanding Image Overlap
The ABC Flight Factor Series
Your image acquisition plan has a large impact on the quality of the results. There are many factors that affect the resulting imagery and project output, but image overlap is one of the most important.The general rule is to maintain a high degree of overlap between images. As a starting point we suggest 75% longitudinal (forward) and 65% lateral (between flying tracks). Understanding image overlap and what the camera sees, will help you get the results you expect the first time
Figure 1: Longitudinal Overlap Example
W hat is overlap?
Image overlap refers to how much of each image captured extends over its surrounding images. It is measured in two ways; longitudinal overlap (front/back) and lateral overlap (side to side).
Why Overlap matters
Where overlap impacts your post processing is in the creation of the final mosaic. Precision software like Pix4D software automatically looks for common tie points between images. The more the images extend over each other, the more matching key points there are and the higher “quality” your resulting mosaic will be.
Much like pieces from a puzzle, each image taken has its place in the final mosaic. Building a 12”x12” puzzle is much easier to put together (mosaic) when there were only 4 pieces than if there are 1000 pieces The large amount of detail in those 4 pieces (images) provides a large amount of unique reference features for the user to use when piecing it together. A 1000-piece puzzle is much more difficult to mosaic as there are only a small amount of detail in each piece for you to reference. The amount of overlap used in a UAV is like the size of the puzzle pieces. The more overlap you use, the more potential you have at connecting unique reference features in one image to its surround images.
As images are captured during a flight, each image has distortion originating from its center point that then extends to the images edges. The type of sensor camera) used will determine the extent of the distortion. Adjusting the percentage overlap compensates for image distortion,
Georeferencing of Images
Overlap also plays a major part when it comes to geo-referencing the mosaic. Each image has its own spatial information attached to it, meaning the geographic position where the sensor took the image is recorded and then embedded as part of the post processing, into the image. When mosaicking, an average is then taken from the spatial information of each image, allowing the final mosaic to be precisely geo-referenced.
Georeferenced images is like having each puzzle piece numbered and associated to a specific position in the puzzle. Image distortion or incorrect spatial referencing in images is like puzzle pieces with defects, i.e. some of the picture is missing, scratched off, etc. If all pieces but one is numbered, you can make an educated guess as to where it would go. Pix4D does not rely on the same ‘instinct’ so in
the post processing to retain quality and accuracy of the output, these image will be omitted.
Next time, before starting out, think about what the camera lens sees in your image (dense forest, water, uniform crop, buildings, mineral piles) in deciding on overlap. There is a balance between job efficiency and resulting image and project quality.
If you have a project type that you fly regularly, experimenting with different overlaps (i.e. same day, height, etc.) and then looking at the output and quality report can help make a better informed decision.
Coming up in continuing this series we will further explore the environmental factors in optimizing your overlap decision.