Semi Automatic Image Classification through Image Segmentation for Land Cover Classification

1. Semi Automatic Image Classification through Image Segmentation for Land Cover Classification Pacific GIS/RS Conference November 2013, NovotelLami Vilisi Tokalauvere SPC/SOPAC

2. Outline • Why Semi-Automatic Image classification? • Tool Used • Problems • Process Framework • Some Preliminary Results

3. Land cover Mapping – 1:10,000 • Enhanced Climate Change Resilience of Food Production Systems (SPC/USAID) • WV2 – 8 Spectral bands • Geo-eye 4 band multi-spectral

5. More detail – More time !

6. Imagine Objective • Additional tool – ERDAS Imagine platform • Feature extraction, update & Change Detection • Produces data in a GIS format

7. Two Approaches

8. IMAGINE Objective • Module for object-oriented geospatial image classification and discrete feature extraction • Single Feature Probability (SFP) Pixel Classification • A novel ERDAS invention applies discriminant analysis to multi-modal training data by distilling samples into Gaussian primitives • Automatic background sampling

9. Initial Roadblocks • Multi – class classification – Less resources available • Whole satellite scene – time consuming • Raw data 16bit– salt and pepper (8bit pan-sharpened) • Experimentation with parameter values

10. First Results

11. IMAGINE Objective ArchitectureProcess Framework Pixels Pixels ( training ) ( candidates ) Prob . Pixels Raster Object Pixel Raster Raster To To Pixel Cue Metrics Probability Object Object Raster Object Raster Object Layer Layer Layer Operator Operator Train Query Pixel Inference Engine Vector Object Raster Object Vector Vector To To Object Object Vector Object Vector Object Layer Layer Operator Operator Vector Vector Objects Objects ( training ) ( candidates ) Vector Object Vector Vector To Object Object Vector Object Layer Layer Operator Object Cue Metrics Train Query Object Inference Engine

12. Methodology • Raster Pixel Processor (RPP) • Performed with Single Feature Probability and Multi – Bayesian Network • System training - Important

13. Methodology • Raster Object Creators (ROC) • Raster Image Created - segmentation • Result – Thematic image

14. Methodology • Raster Object Operators (ROO) • Size filter • Probability filter • Eliminating raster objects that do not meet criteria

15. Methodology • Raster to Vector Conversion (RVC) • Raster object vectorised by ‘polygon trace’ • Polygons or Polylines produced • Vector Object Operators • Reshaping the existing Vector Objects, eliminating vector objects that do not meet some criteria, combining multiple input vector objects into a single vector object, splitting vector objects into multiple new vector objects

16. Easy Editing

17. Vector Object Classification • The Vector Object Processor (VOP) node performs classification on vector objects. • Vector Cleanup Operators (VCO) allow the user to manipulate the Vector Objects after they have been processed by the Vector Object Processor.

18. Preliminary Results

19. Preliminary Results

20. Visual Interpretation after Segmentation

21. Vinaka