Hello,
i’m taking the liberty to post a message in order to share an use case.
Our need/purpose is to find existing methods/tools that would allow to perform segmentations with the aim of helping botanists for pre-map physiognomic units before to go on the field. In other words : to produce a delineation (polygonal layer) of physiognomic units. In my humble opinion, this is an unusual need/use case because the segmentation is NOT intended/aimed as a first step before classification. The goal is to reproduce as closely/faithfully as possible the botanist’s photo-interpretation. We need to find the right balance/parameters between the number of segments, their sizes, and their compactness (shape). The idea would be to perform relevant segmentation across the entire area being processed, neither over-segmented nor under-segmented.
With optimal parameters/configuration and input variables/data that can vary depending on the environment (urban, agricultural, wooded, herbaceous).
I’ve done some tests, notably with OTB Approaches Region Merging:
https://www.orfeo-toolbox.org/CookBook-7.0/Applications/app_GenericRegionMerging.html
I haven’t really tested other traditional approaches (such as cluster mean shift or watershed, for example). Until now, I haven’t tested AI/DL because I thought there wouldn’t be any segmentation models trained on IRC ortho DB images that could meet our needs.
This is why I prioritized “traditional” segmentation tools/approaches.
My tests were conducted using satellite spot6/7 images and Infrared Colour aerial orthographic databases (franch mapping agency). Tests with the aerial orthographic database are more resource-intensive and time-consuming. Furthermore, this OTB tool tested (GRM) is not suitable for large images. The results are quite good, particularly for agricultural and urban areas.
For forested areas, it’s more complicated. And in order to improve the results, it may be necessary to consider integrating textures or indicators (NDVI or other) ?
if you have any comments or suggestions for guidance regarding methods/tools/data, I would be happy to read you.
Thank you in advance.
Best regards,