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

Vinaka