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Manual/Automatic classification and segmentation
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Manual/Automatic classification and automatic segmentation for small photogrammetric datasets.
Goal: extracting rocks from the ambiant background (ground) and segment them so that you can export individual point clouds for further processing.
Methodology:
1. Manual classification with heightmap.
The easiest way to classify your data if you have a highly contrasted and flat dataset (which is almost never)
- Clone you PCL (pointcloud) to keep the original RGB information somewhere (if relevant)
- Compute the heightmap as RGB
- Convert the RGB values as Scalar Fields
- Pick the relevant classification values with the Scalar Field histogram
- Proceed with "select by values" to extract the relevant part of your data
- Start again if you need multiple classification parameters
- Clear the rgb colors from each extracted PCL and transfer the RGB values from the cloned PCL (if relevant again)
A more robust alternative
- It does not work with very small datasets (here around 4m²) so we have to scale up the PCL to trick the plugin into thinking it's a relatively big area
- Still, I recommend using the finest settings to get good results with this very example
- In the end, you get two PCL with extracted features
3. Automatic segmentation
- If your extracted features which are somehow isolated one from another, you can run the segmentation tool (Tools - Segmentation - Label Conncted Comp)
- You get in return a list of each feature as a separate PCL ranked in descending order of volume
- The point here was very specific because we need to export each feature separatly to run surface and volume analysis in another software.
Goal: extracting rocks from the ambiant background (ground) and segment them so that you can export individual point clouds for further processing.
Methodology:
1. Manual classification with heightmap.
The easiest way to classify your data if you have a highly contrasted and flat dataset (which is almost never)
- Clone you PCL (pointcloud) to keep the original RGB information somewhere (if relevant)
- Compute the heightmap as RGB
- Convert the RGB values as Scalar Fields
- Pick the relevant classification values with the Scalar Field histogram
- Proceed with "select by values" to extract the relevant part of your data
- Start again if you need multiple classification parameters
- Clear the rgb colors from each extracted PCL and transfer the RGB values from the cloned PCL (if relevant again)
A more robust alternative
- It does not work with very small datasets (here around 4m²) so we have to scale up the PCL to trick the plugin into thinking it's a relatively big area
- Still, I recommend using the finest settings to get good results with this very example
- In the end, you get two PCL with extracted features
3. Automatic segmentation
- If your extracted features which are somehow isolated one from another, you can run the segmentation tool (Tools - Segmentation - Label Conncted Comp)
- You get in return a list of each feature as a separate PCL ranked in descending order of volume
- The point here was very specific because we need to export each feature separatly to run surface and volume analysis in another software.
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