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General / Re: Your opinion on USGS Agisoft Processing Workflow
« on: April 10, 2020, 01:49:26 AM »
Hello all,
This may not fall directly under the topic of this thread, but it seems there are lots of experienced metashape users with helpful insights on here, so here we are.
I am working with two fairly small sets of historic aerial photos (30-50 images each, from 1943 and 1978), and have some questions about what the error rates mean and how I might improve my workflow. I am not certain of exactly how the images were digitized, but I believe they came from negatives scanned in with a high quality (but not photogrammetric) scanner, and are stored as .tifs. After initially aligning, I have been incorporating the basic elements of the USGS workflow mentioned above: editing the sparse cloud by reconstruction uncertainty and projection accuracy, importing control points, editing by reprojection error, and optimizing cameras after each step. The dense point cloud and DEM generated from the images has too much noise and too many holes to produce a good orthomosaic, so I have been using a lidar DEM and have gotten better results with that.
The fiducial marks on the images are not auto-detectable so I have so far not used them (I picked up this project fairly recently), but I would be very interested to hear about anyone's experience using them/how much difference they make.
So far, camera errors for one set are pretty rough (1-2px), with better control point error. The orthomosaic has few visual errors in my area of interest (near the center of the scene, with good dispersal of GCPs, and at higher elevations than the surrounding area), but significant visual errors in the valleys. The images for this set are not cropped perfectly - there is still some border on one side of the image, and some of the fiducials are cut off - so that may be hampering the quality of my results, but I am unsure of how much difference that makes.
The other set has much better camera error (~0.4px) and control point error, and the orthomosaic is good but still has some relatively small visual errors (disjointed roads, duplicated areas over seams, etc).
I have two main questions: For the worse image set, placing more GCPs in the scene increases the camera error and disperses the visual errors across the scene, so that the valleys aren't as bad but the uplands (where I'm interested) are worse. Why might this happen? Is it the lack of geometric correction from fiducials, poor overlap, poor quality GCPs (all of which are possible)?
Second, regarding the better image set, how are the camera errors, control point errors, and visual errors related? My goal is to measure vegetation change with a supervised classification, so it seems to me like the visual errors in both orthomosaics would harm the accuracy of this measurement; but maybe a lower camera error produces better results across the whole image, while the visual errors are isolated to the seamlines?
I am quite new to metashape and photogrammetry, and any help would be much appreciated.
This may not fall directly under the topic of this thread, but it seems there are lots of experienced metashape users with helpful insights on here, so here we are.
I am working with two fairly small sets of historic aerial photos (30-50 images each, from 1943 and 1978), and have some questions about what the error rates mean and how I might improve my workflow. I am not certain of exactly how the images were digitized, but I believe they came from negatives scanned in with a high quality (but not photogrammetric) scanner, and are stored as .tifs. After initially aligning, I have been incorporating the basic elements of the USGS workflow mentioned above: editing the sparse cloud by reconstruction uncertainty and projection accuracy, importing control points, editing by reprojection error, and optimizing cameras after each step. The dense point cloud and DEM generated from the images has too much noise and too many holes to produce a good orthomosaic, so I have been using a lidar DEM and have gotten better results with that.
The fiducial marks on the images are not auto-detectable so I have so far not used them (I picked up this project fairly recently), but I would be very interested to hear about anyone's experience using them/how much difference they make.
So far, camera errors for one set are pretty rough (1-2px), with better control point error. The orthomosaic has few visual errors in my area of interest (near the center of the scene, with good dispersal of GCPs, and at higher elevations than the surrounding area), but significant visual errors in the valleys. The images for this set are not cropped perfectly - there is still some border on one side of the image, and some of the fiducials are cut off - so that may be hampering the quality of my results, but I am unsure of how much difference that makes.
The other set has much better camera error (~0.4px) and control point error, and the orthomosaic is good but still has some relatively small visual errors (disjointed roads, duplicated areas over seams, etc).
I have two main questions: For the worse image set, placing more GCPs in the scene increases the camera error and disperses the visual errors across the scene, so that the valleys aren't as bad but the uplands (where I'm interested) are worse. Why might this happen? Is it the lack of geometric correction from fiducials, poor overlap, poor quality GCPs (all of which are possible)?
Second, regarding the better image set, how are the camera errors, control point errors, and visual errors related? My goal is to measure vegetation change with a supervised classification, so it seems to me like the visual errors in both orthomosaics would harm the accuracy of this measurement; but maybe a lower camera error produces better results across the whole image, while the visual errors are isolated to the seamlines?
I am quite new to metashape and photogrammetry, and any help would be much appreciated.
