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Classification
Using Groups
Arttu Soininen 19.01.2017
Classification Using Groups – Why?
• Better automatic classification of above ground features
• Faster manual classification of above ground features
Old Classification Tools
• Most old classification routines classify points – make a decision if one point should
be classified or not
• Some old classification routines have internally formed groups of points
• Building classification has formed groups of planar points
• Tree classification has formed a group under a local highest point
• Each routine has had its own grouping principle
• No way to evaluate if a group is more like a tree than like a building
Classification Using Groups
• Run grouping of above ground points
• Goal is to have each object as one
group of points
• Software stores group value for each
point in FastBinary file format
• Manual and automatic classification
can work on object level
Assign groups
• Builds groups from points in source classes
• Typically high vegetation or medium+high vegetation
• Can use four different grouping principles:
• Group by selected polygons creates one group inside each selected
polygon
• Group planar surfaces finds large enough planar surfaces such as
roofs or walls
• Group by tree logic finds groups using watershed algorithm starting
from highest local point
• Group by density uses spacing between points
Group Numbers & Project
• Each project block should have its own default block number range or at least
blocks neighbouring each other should have different block numbers ranges
• Block numbers are unsigned 32 bit integers from 1 to 4294967295
• Project definition has Group count setting which reserves group numbers for each
block you add
• 1000000 group count per block works with upto 4294 blocks
• 100000 group count per block works with upto 42949 blocks
• At block borders an extra processing step is needed to force a group to have same
number in all blocks
Fix border groups
• Macro action which assigns matching group numbers to groups overlapping borders
• Final group number comes from the block which has biggest point count in that
group
• Result may have more mismatches if some of the points in a block are outside block
boundaries (for example after applying HRP correction in TerraMatch)
• You need to:
• Run a macro on a project with Assign groups step
• Run a second macro with Fix border groups step
• You should use same Neighbours setting in both runs
Create Point Group
• Creates a new group from points inside a fence or starting with highest point of a tree
• You would typically use this tool when automatic grouping has placed two or more
objects into the same group
Merge Point Groups
• Merge two or more groups into one
• First mouse click identifies master group
• Additional mouse clicks identify groups to merge into master
Classify Groups / By best match
• Software can evaluate each group using multiple object
recognition routines
• Classifies each group to best matching class
• Example: software may evaluate one group to be:
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Building roof with 0% probability
Building wall with 0% probability
Tree with 77% probability
Pole with 42% probability
Vegetation with 58% probability
Car with 0% probability
Classify Groups / By class
• Classifies groups to one destination class
• Can filter groups to classify by source class and by how many
points are inside fence
Classify Groups / By distance
• Classifies groups by distance values
• Each point in a group has its own distance value
• Classification can be based on Biggest, Median, Average or
Smallest of those distance values
Group / Test parameters
• Software can compute a number of statistical parameters for each point group
• Test parameters finds what statistical parameters can separate object types from
each other (for example different tree species from each other)
• Tool requires that user has manually classified example groups
• User can then 'teach' the software to recognize object types
Classify Groups / By parameters
• Classifies groups by statistical parameters
• User must have created a parameter settings file using Group / Test parameters
Processing Steps for Airborne LIDAR
• Classify ground
• Classify wires if needed
• Use compute distance to compute height above ground value for each point
• Classify medium vegetation using 'Classify / By distance'
• Classify high vegetation using 'Classify / By distance'
• Compute normal vectors using 'Tools / Compute normal vectors'
• Group points using 'Group / Assign groups'
• Classify groups using 'Group / Classify / By best match' and other group based
routines