e-mail: [email protected], [email protected],

Remote Sensing LaboratoryDept. of Information Engineering and Computer Science

University of TrentoVia Sommarive, 14, I-38123 Povo, Trento, Italy

A NOVEL ACTIVE LEARNING STRATEGY FOR DOMAIN ADAPTATION IN THE CLASSIFICATION OF

REMOTE SENSING IMAGES

e-mail: [email protected], [email protected], Web page: http://rslab.disi.unitn.it

C. Persello L. Bruzzone

University of Trento, Italy 2C. Persello, L. Bruzzone

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Outline

Background on Domain Adaptation and Active Learning

Aim of the Work

Proposed Approach to Address Domain Adaptation Problems with Active Learning

Experimental Results

Conclusions

University of Trento, Italy

Introduction

Scenario: Growing availability of space-borne data that gives the opportunity to develop several applications related to land-cover mapping and monitoring.

Problem: Common automatic classification techniques are based on supervised learning methods, which require a set of new training samples every time that a new remote sensing image has to be classified

Need for the development of efficient techniques capable to adapt the supervised classifier trained on a image for the classification of another similar but not identical image acquired either:

1) on a different area, or 2) on the same area at a different time.

C. Persello, L. Bruzzone 3


Background on Domain Adaptation

Domain Adaptation: models the problem of adapting a supervised classifier trained on a given image (source domain) to the classification of another similar but not identical image (target domain) acquired either on a different area, or on the same area at a different time.

Assumption: Source and target domain share the same set of land cover classes.

C. Persello, L. Bruzzone

[1] L. Bruzzone, D. Fernandez Prieto, “Unsupervised retraining of a maximum-likelihood classifier for the analysis of multitemporal remote-sensing images,” IEEE Trans. Geosci. Remote Sens., Vol. 39, No.2, pp. 456-460, 2001.

[2] L. Bruzzone, M. Marconcini, “Domain Adaptation Problems: a DASVM Classification Technique and a Circular Validation Strategy,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 32, 2010, No. 5, pp. 770-787, 2010.

Source Domain Target DomainSemisupervised

techniques (e.g., [1], [2])

Problem: correct converngence is

not always possibleClass

Class

Class Unknown Class

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Working Assumption

Working Assumption: In this work we assume that some samples (as little as possible) from the target domain can be labeled by the user and added to the existing training set.

Proposed solution: use of Active Learning [1], [2] procedure for selecting the most informative samples of the target domain.


Update T GTi-1 Ti classification

QSX

UGeneral Active

ProcessG: Supervised classifier;Q: Query function;S: Supervisor; T: Training set;U: Unlabeled data

[1] S. Rajan, J. Ghosh, and M. M. Crawford, “An active learning approach to hyperspectral data classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 4, pp. 1231-1242, Apr. 2008.[2] B. Demir, C. Persello, and L. Bruzzone, “Batch mode active learning methods for the interactive classification of remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no.3, pp. 1014-1031, March 2011.

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Aim of the Work

Aim of the Work: propose a novel Domain Adaptation technique based on Active Learning, which aims at classifying the target image, while requiring the minimum number of labeled samples from the new image.

Basic Idea: iterative process based on1) labeling and adding to the training set the most informative samples from the target

domain (query+), while2) removing from the training set the source-domain samples that do not fit with the

distributions of the classes in the target domain (query-).

Example:


Source Domain Target Domain

Query+Query-

Class

Class

Class

Convergence reached!

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Proposed Technique


Classification technique: Gaussian Maximum Likelihood,

Query+: selects the batch of the most informative samples from the pool of unlabeled samples, which are taken from the target domain.

x

𝑝 (𝑖 ) (𝐱∨𝜔1 ) 𝑝 (𝑖 ) (𝐱∨𝜔2 ) 𝑝 (𝑖 ) (𝐱∨𝜔3 )

𝐱+¿¿

Largest class-conditional density

Second largest class-conditional density

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Proposed Technique


Query-: removes from the source-domain training set the labeled samples that do not fit with the distribution of the classes in the target domain.

x- x

𝑝 (0) (𝐱−∨𝜔1 )

𝑝 (𝑖 ) (𝐱−∨𝜔1)

𝑝 (0) (𝐱∨𝜔1 )𝑝 (𝑖 ) (𝐱∨𝜔1 )

𝑝 (0) (𝐱∨𝜔2 )𝑝 (𝑖 ) (𝐱∨𝜔2 )

Class-conditional density computed using source-domain samples

Class-conditional density computed using samples at iteration i

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Proposed Technique


Combination of Query+ and Query-: Both queries work at the same time on the basis of the following parameters: number of samples selected by q+; number of samples selected by q-;

Stop Criterion: we considered the Bhattacharyya distance:

The active learning process is stopped when reaches a stable saturation point. This allows the user to detect the convergence of the algorithm without a test set on the target domain

Class-conditional density computed using source-domain samples

Class-conditional density computed using samples at iteration i

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𝐵 (𝑖 )= 1𝐶 ∑

𝑛=1

𝐶

𝐵𝑛 (𝑖 )𝐵𝑛 (𝑖 )=−ln {∫𝒙❑

√𝑝 ( 0) (𝐱∨𝜔n )𝑝 (𝑖 ) (𝐱∨𝜔n )}


Data Set Description: VHR data set


Data set: Two Quickbird images acquired in 2006 over two rural areas in Trento, Italy.Reference labeled data: Two sets of labeled samples for each image.Land-cover classes: Vineyard, water, agriculture fields, forest, apple tree, urban area.

Image QB1

Image QB2

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Data Set Description


Distribution of labeled samples on bands 3 and 4 of the two Quickbird images

50 100 150 200 250 3000

100

200

300

400

500

600

700

band 3

band

4

VineyardWaterAgriculture FieldsForestApple TreeUrban Area

50 100 150 200 250 3000

100

200

300

400

500

600

700

band 3ba

nd 4

VineyardWaterAgriculture FieldsForestApple TreeUrban Area

Source Domain Target Domain

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10 initial training sets optimized for the classification of QB1

Initial training set size: 965 samples

For the proposed technique we used:



0 100 200 300 400 500 60065

70

75

80

85

90

Number of Labeled Samples of the Target Domain

Ove

rall

Acc

urac

y (%

) on

TS

2

AL on QB2AL random on QB2q+q+ randomProposed DA method (q+ and q-)

0 100 200 300 400 500 6000

0.5

1

1.5

2

2.5

3


Bha

ttach

aryy

a D

ista

nce

VineyardWaterAgriculture FieldsForestApple TreeUrban AreaAverage

Averaged learning curves over ten trials

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Data Set Description: hyperspectral data set

Study area: Okavango Delta, Botswana.

Data set: Hyperspectral image acquired by the Hyperion sensor of the EO-1 satellite (145 noise free bands).

Classes: 14 different land-cover types.

Reference labeled data was collected in two disjoint areas and four different sets were defined:

• a training set T1

• a spatially correlated test set TS1

• a training set T2 spatially disjoint from T1

• a test set TS2 spatially correlated with T2


T1

T2

TS1

TS2

Area 1

Area 2

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10 initial training sets optimized for the classification of Area 1

Initial training set size: 707 samples

For the proposed technique we used:



Averaged learning curves over ten trials

0 100 200 300 400 500 60065

70

75

80

85

90

95

100


Ove

rall

Acc

urac

y (%

) on

TS

2

AL on Area 2AL random on Area 2q+q+ randomProposed DA method (q+ and q-)

0 100 200 300 400 500 6000

0.5

1

1.5

Number of Labeled Samples of the Target DomainA

vera

ge B

hatta

char

yya

dist

ance

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University of Trento, Italy C. Persello, L. Bruzzone

A novel approach to address Domain Adaptation problems with Active Learning has been proposed.

Assuming that an image and the related reference labeled samples are available, the proposed technique can be used either:

1) to classify another image acquired on another geographical area with similar characteristics and the same land-cover classes, or

2) to update the land-cover map given a new image acquired on the same area at a different time.

We introduced a stop criterion that does not require a test set defined on the target domain.

Future Developments:

Include a diversity criterion in the query+ function.

Extend the proposed method to kernel-based classifiers.

Conclusion

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e-mail: [email protected], [email protected],

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