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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

Semi Automatic Image Classification through Image Segmentation for Land Cover Classification

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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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Page 1: Semi Automatic Image  Classification  through Image  Segmentation  for Land Cover Classification

Semi Automatic Image Classification through Image Segmentation for

Land Cover Classification

Pacific GIS/RS Conference November 2013,

Novotel LamiVilisi Tokalauvere

SPC/SOPAC

Page 2: Semi Automatic Image  Classification  through Image  Segmentation  for Land Cover Classification

OutlineWhy Semi-Automatic Image classification?

Tool UsedProblemsProcess FrameworkSome Preliminary Results

Page 3: Semi Automatic Image  Classification  through Image  Segmentation  for Land Cover Classification

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

Page 4: Semi Automatic Image  Classification  through Image  Segmentation  for Land Cover Classification
Page 5: Semi Automatic Image  Classification  through Image  Segmentation  for Land Cover Classification

More detail – More time !

Page 6: Semi Automatic Image  Classification  through Image  Segmentation  for Land Cover Classification

Imagine Objective • Additional tool – ERDAS

Imagine platform• Feature extraction, update &

Change Detection• Produces data in a GIS format

Page 7: Semi Automatic Image  Classification  through Image  Segmentation  for Land Cover Classification

Two Approaches

Page 8: Semi Automatic Image  Classification  through Image  Segmentation  for Land Cover Classification

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

Page 9: Semi Automatic Image  Classification  through Image  Segmentation  for Land Cover Classification

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

Page 10: Semi Automatic Image  Classification  through Image  Segmentation  for Land Cover Classification

First Results

Page 11: Semi Automatic Image  Classification  through Image  Segmentation  for Land Cover Classification

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

Page 12: Semi Automatic Image  Classification  through Image  Segmentation  for Land Cover Classification

Methodology• Raster Pixel Processor (RPP)– Performed with Single Feature

Probability and Multi – Bayesian Network– System training - Important

Page 13: Semi Automatic Image  Classification  through Image  Segmentation  for Land Cover Classification

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

Page 14: Semi Automatic Image  Classification  through Image  Segmentation  for Land Cover Classification

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

criteria

Page 15: Semi Automatic Image  Classification  through Image  Segmentation  for Land Cover Classification

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

Page 16: Semi Automatic Image  Classification  through Image  Segmentation  for Land Cover Classification

Easy Editing

Page 17: Semi Automatic Image  Classification  through Image  Segmentation  for Land Cover Classification

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.

Page 18: Semi Automatic Image  Classification  through Image  Segmentation  for Land Cover Classification

Preliminary Results

Page 19: Semi Automatic Image  Classification  through Image  Segmentation  for Land Cover Classification

Preliminary Results

Page 20: Semi Automatic Image  Classification  through Image  Segmentation  for Land Cover Classification

Visual Interpretation after Segmentation

Page 21: Semi Automatic Image  Classification  through Image  Segmentation  for Land Cover Classification

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