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Semi Automatic Image Classification through Image Segmentation for Land Cover Classification. Pacific GIS/RS Conference November 2013, Novotel Lami Vilisi Tokalauvere SPC/SOPAC. Outline. Why Semi-Automatic Image classification? Tool Used Problems Process Framework - PowerPoint PPT Presentation
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Semi Automatic Image Classification through Image Segmentation for
Land Cover Classification
Pacific GIS/RS Conference November 2013,
Novotel LamiVilisi Tokalauvere
SPC/SOPAC
OutlineWhy Semi-Automatic Image classification?
Tool UsedProblemsProcess FrameworkSome Preliminary Results
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
More detail – More time !
Imagine Objective • Additional tool – ERDAS
Imagine platform• Feature extraction, update &
Change Detection• Produces data in a GIS format
Two Approaches
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
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
First Results
IMAGINE Objective ArchitectureProcess Framework
VectorObjects
(training )
VectorObjects
( candidates )
Train Query
Object Cue Metrics
ObjectInference
Engine
Raster ObjectTo
Raster ObjectOperator
PixelProbability
Layer
RasterObjectLayer
VectorObjectLayer
RasterObjectLayer
Raster ObjectTo
Vector ObjectOperator
Vector ObjectTo
Vector ObjectOperator
VectorObjectLayer
VectorObjectLayer
Prob . PixelsTo
Raster ObjectOperator
VectorObjectLayer
Vector ObjectTo
Vector ObjectOperator
Pixels(training )
Train Query
PixelInference
Engine
Pixels( candidates )
Pixel Cue Metrics
Methodology• Raster Pixel Processor (RPP)– Performed with Single Feature
Probability and Multi – Bayesian Network– System training - Important
Methodology• Raster Object Creators (ROC)– Raster Image Created - segmentation– Result – Thematic image
Methodology• Raster Object Operators (ROO)– Size filter– Probability filter– Eliminating raster objects that do not meet
criteria
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
Easy Editing
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.
Preliminary Results
Preliminary Results
Visual Interpretation after Segmentation
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