IMPROVING ACTIVE LEARNING METHODSUSING SPATIAL INFORMATION

IGARSS 2011

Edoardo PasolliUniv. of Trento, Italypasolli@disi.unitn.it

Farid MelganiUniv. of Trento, Italy

July 28, 2011

Devis TuiaUniv. of València,

Spain

Fabio PacificiDigitalGlobe, Colorado

William J. EmeryUniv. of Colorado at

Boulder

Introduction

Supervised classification approach

2

Pre-processing

Feature extraction

Classification

Image/Signal Decision

Training sample

collection

Training sample quality/quantity

Human expert

Impact on accuracies

Introduction

Active learning approach

3

Trainingof classifier

Active learningmethod

Model of classifier

Learning (unlabeled) set

Labeling of selected samples

Selected samplesafter labeling

Insertion in training set

f1

f2

f1

f2

f1

f2

Selected samples from

learning (unlabeled) set

f2

f1

f2

f1

Human expert

Training (labeled) set

Class 1 Class

3

Class 2

Objective

Propose SVM-based active learning strategy for classification of remote sensing images by combining spectral and spatial information

4

Support Vector Machines (SVMs) Training set: Kernel function:

Dual optimization problem

maximize

subject to

5

n

jijijiji

n

ii Kyy

1,1

),(2

1xx

niC

y

i

n

iii

,...,1 ,0

01

niii y 1, x

'K xx,

Support Vector Machines (SVMs) Discriminant function

6

SVMTraining

f1

f2

Training (labeled) set

in feature space

Class 1

Class 2 f1

f2

Training (labeled) set

in feature space

SVM model

0 absolute value ofdiscriminant function

: SV

n

i

*iii bKyf

1

* ),()( xxx

Proposed Strategy7

L: Training setSVs: Support vectors

SVMTraining

Spectral selectioncriterion

Spatial selectioncriterion

U’s: Selected

samplesSelection

Insertion intraining set

Us: Sorted samples

U: Learning set

Human expert

Nondominated

sorting

Labeling

L’s: Labeled samples

Spectral Criterion: Margin Sampling (MS)

8

Selection

Learning (unlabeled) setin feature space

f1

f2

f1Training (labeled)

setin feature space

f2

SVM model

f1

f2

Selected samples from

learning (unlabeled) set

in feature space

selection of samples with minimum absolute values of

discriminant function

0 absolute value ofdiscriminant function

: SV

Spatial Criterion: Distance from SVs (Sp)

9

Selection

selection of samples with maximum distance values from

the closest SV

: SV

0- distance valuefrom the closest SV

f1Training (labeled)

setin feature space

f2

SVM model

Learning (unlabeled) set in

spatial space

Selected samples from

learning (unlabeled) set in spatial space

Training (labeled) set

in spatial space

Combined Criterion (MS&Sp)10

determined by nondominated

sorting

selection of samplesstarting from the Pareto Front

1 front number

Front 1: Pareto Front

Front 2

Front 3

Front 4

Front 5

MS: absolute value of

discriminant function

Sp: - distance value from the

closest SV

Experimental Results

Data set description Test site: Las Vegas, Nevada Acquisition date: 2002 Sensor: QuickBird # features: 4 spectral +

36 morphological Spatial resolution: 0.6 m # thematic classes: 11

11

False color compositing

Commercial buildings

Residential houses

Drainage channel

Roads

Trees

Short vegetation

Water

Bare soil

Parking lots

Soil

Highways

Experimental Results

Data set description Test site: Las Vegas, Nevada Acquisition date: 2002 Sensor: QuickBird # features: 4 spectral +

36 morphological Spatial resolution: 0.6 m # thematic classes: 11

12

Ground truth

Commercial buildings

Residential houses

Drainage channel

Roads

Trees

Short vegetation

Water

Bare soil

Parking lots

Soil

Highways

Experimental Results13

Training set MS criterion

Sp criterion MS+Sp criterion

0

absolute value ofdiscriminant function

0

- distance value fromthe closest SV

1

front number

Experimental Results

Overall Accuracy and Kappa index

14

Experimental Results

Absolute value of discriminant functionnormalization of map standard

deviation

15

Experimental Results

Detailed results

16

Accuracies on 343,023 test samples

Method#

trainingsamples

OA σOAKapp

aσKappa AA σAA σDF

Full 30000 95.47 - 0.947 - 93.35 - 0.06Initial 55 58.98 5.74 0.533 0.06 59.33 4.10 0.45

RMS

MS&Sp1035

84.8988.0989.73

0.560.400.24

0.8230.8600.880

0.0070.0050.003

79.2283.1584.86

1.470.800.38

0.250.250.16

RMS

MS&Sp2035

87.1890.5492.13

0.420.890.19

0.8500.8890.908

0.0050.010.01

82.0086.5588.39

1.001.070.27

0.220.180.16

Experimental Results

Detailed results

17

Accuracies on 343,023 test samples

Method#

trainingsamples

OA σOAKapp

aσKappa AA σAA σDF

Full 30000 95.47 - 0.947 - 93.35 - 0.06Initial 55 58.98 5.74 0.533 0.06 59.33 4.10 0.45

RMS

MS&Sp1035

84.8988.0989.73

0.560.400.24

0.8230.8600.880

0.0070.0050.003

79.2283.1584.86

1.470.800.38

0.250.250.16

RMS

MS&Sp2035

87.1890.5492.13

0.420.890.19

0.8500.8890.908

0.0050.010.01

82.0086.5588.39

1.001.070.27

0.220.180.16

Experimental Results

Detailed results

18

Accuracies on 343,023 test samples

Method#

trainingsamples

OA σOAKapp

aσKappa AA σAA σDF

Full 30000 95.47 - 0.947 - 93.35 - 0.06Initial 55 58.98 5.74 0.533 0.06 59.33 4.10 0.45

RMS

MS&Sp1035

84.8988.0989.73

0.560.400.24

0.8230.8600.880

0.0070.0050.003

79.2283.1584.86

1.470.800.38

0.250.250.16

RMS

MS&Sp2035

87.1890.5492.13

0.420.890.19

0.8500.8890.908

0.0050.010.01

82.0086.5588.39

1.001.070.27

0.220.180.16

Conclusions

In this work, new SVM-based active learning strategy by combining spectral and spatial information is proposed

Encouraging performances in terms of classification accuracy: convergence speed and

stability classification reliability

Drawbacks higher computational load

19

IMPROVING ACTIVE LEARNING METHODSUSING SPATIAL INFORMATION

IGARSS 2011

Edoardo PasolliUniv. of Trento, Italypasolli@disi.unitn.it

Farid MelganiUniv. of Trento, Italy

July 28, 2011

Devis TuiaUniv. of València,

Spain

Fabio PacificiDigitalGlobe, Colorado

William J. EmeryUniv. of Colorado at

Boulder