I am starting this discussion to bring to together all of the different types of software people are using to process photos collected with their drones and also what they are using it for and what limitations they are finding.
I am currently using the following software:
Agisoft Photoscan Pro
-The data I am running through it was not collected for photogrammetry so I am having some difficulties
-Some images are collected in winter with on the ground making it harder for photoscan to find matching points
-Some images do not have enough overlap or not good enough quality
-Images over forested landscapes sometimes have problems finding matching points
I have using this for quick stitching of images with too little overlap for Photoscan.
I have found it does not work well for long linear set of images
I use google earth to find coordinates for ground control points or georeferencing.
Is an open source GIS software that I use for georeferencing stitched or single images and creating data from the images
I also do a lot of work with LiDAR data and as such am very interested in classifying the point cloud that I can create with Photoscan. The new version has a tool for classifying ground points and then allowing you manually sort the rest. But I am also interested in using the one thing the advantage that photogrammetrically derived points clouds have over raw Lidar data and that is point cloud colours. I am interested in creating a work flow to classify orthos created into feature types (as can already be done) and then assigning these feature type to the point cloud that is also created.
I will do that, thanks :)
I really like Agisoft Photoscan Pro, it would just be nice to create a completey open source and free work flow for anyone to use for any purpose.
Anyways here is a comparison of some results I got from Pix4UAV:
And the same dataset run through Agisoft Photoscan:They both took around the same amount of time to process (actually Photoscan may have been a bit faster). Visually the output from Photoscan is much cleaner, less noise, colours where they should be. Spatially they are both in the same spot. It just seems that Photoscan picked out finer details.
Now the next step is to classify the point cloud data. I am think that classifying the orthophoto into polygons representing different features and then assigning these polygons to the point cloud is the way to go.
Some of the different options I am looking at include:
Do not hesitate to post your results.
I'll be really interested about them !
You're using the same data I played with to verify how to generate scaled and georeferenced point clouds. I used the roof of that house in the middle, measured this on google earth and then using rapid explorer in the point cloud. I got some great results, but now I need to reverify my results using a more accurate data set with ground control points.
It has GCPs see link above to download them.
I can't seem to find this Rapid Explorer you are mentioning? Can you share a link?
Also here are some links to download the data I processed (I didn't use the ground control points):
Both the point cloud and orthophoto are in WGS84. Another tool that I have failed to mention that I like to use is lastools. It is 'free', but not open source, not for commercial use. It is designed to be used on LiDAR point clouds but does work for photogrammetry point clouds as well.
It got renamed:
I use cloudcompare to read laz data.
I can't find the dataset you are talking about.
The only page I've found is this one:
And there is no download link.
Could you please tell me where did you find the dataset ?
Thanks, I am trying it out.
I had to create a trial account with them and log in. Once logged in I had access to the download. If you already have the photos downloaded here is a link to access the GCP data (I have it stored on my google drive):
Agisoft Pro is certainly the best quality for the price, in addition to their constant updates free for two years. We have been working with Agisoft Pro for a year now and the results are expectaculares in resolution, precision and products generated, although we must not forget that the camera should be of good optical quality with a better final product quality. Also important is an overlap of more than 60% to 80% orthoimage and digital surface models.