Classification Using Groups

Transcription

1 Classification Using Groups Arttu Soininen

2 Classification Using Groups Why? Better automatic classification of above ground features Faster manual classification of above ground features

3 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

4 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

5 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

6 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 Project definition has Group count setting which reserves group numbers for each block you add group count per block works with upto 4294 blocks group count per block works with upto blocks At block borders an extra processing step is needed to force a group to have same number in all blocks

7 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

8 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

9 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

10 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: 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

11 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

12 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

13 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

14 Classify Groups / By parameters Classifies groups by statistical parameters User must have created a parameter settings file using Group / Test parameters

15 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