remote sensing laboratory dept. of information engineering and computer science university of trento...

Remote Sensing LaboratoryDept. of Information Engineering and Computer Science

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

Remote Sensing LaboratoryDept. of Information Engineering and Computer Science

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

Begum DemirBegum DemirFrancesca BovoloFrancesca BovoloLorenzo BruzzoneLorenzo Bruzzone

Detection of Land-Cover Transitions in Multitemporal Remote Sensing Images

with Active Learning Based Compound Classification

Detection of Land-Cover Transitions in Multitemporal Remote Sensing Images

with Active Learning Based Compound Classification

E-mail: [email protected]: [email protected] page: http://rslab.disi.unitn.itWeb page: http://rslab.disi.unitn.it

University of Trento, Italy

Outline

2B. Demir, F. Bovolo, L. Bruzzone

Introduction

Aim of the work

1

Conclusion and future developments

Proposed Joint Entropy based Active-Learning Method for Compound Classification

2

3

5

Experimental results4

University of Trento, Italy 3

Detection of land-cover transitions between a pair of remote sensing images acquired on the same area at different times (i.e., multitemporal images) is very useful in many applications.

Usually, this is achieved by supervised classification techniques, as unsupervised change detection methods have a reduced reliability in detecting explicitly different land-cover transitions.

Such an approach requires ground reference data to detect changes and identify transitions .

Due to the properties of the last generation of VHR passive sensors, supervised change-detection methods in real applications is becoming more and more important.

Problem: The collection of a large multitemporal reference data is time consuming andexpensive.

Introduction

B. Demir, F. Bovolo, L. Bruzzone

University of Trento, Italy 4

Goals

Compute a map of land-cover transitions between a pair of remote sensing images acquired on the same area at different times.

Take advantage of temporal dependence between images.

Define a training set as small as possible.

Assumptions

The same set of land-cover classes characterizes the images.

Initial training set with small number of labeled samples is available.

Solution: Develop a novel Active Learning (AL) technique for compound classification of multitemporal remote sensing images that takes advantage of the temporal dependence among images.

Aim of the Work


University of Trento, Italy 5B. Demir, F. Bovolo, L. Bruzzone

G: Supervised classifier; Q: Query function; S: Supervisor;

T: Training set; U: Unlabeled data I: Image

Active Learning for Single Image Classification

[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.

Update T GT

G

General AL Scheme

Expanded T

S Q

U

I


Proposed Method: Block Scheme


X1

X2

t1 image

Active Learning

Compound Classifier

t2 image Map of land-cover transitions

Different kinds of changes

Training Set (pairs of temporally

correlated labeled samples)

Expanded Training Set




X1

X2

t1 image

Active Learning

Compound Classifier







Proposed Method: Compound Classifier


X1

X2

t1 image

Compound Classifier

Estimationof Classifier Parameters



L. Bruzzone, D. Fernandez Prieto, and S.B. Serpico, “A neural-statistical approach to multitemporal and multisource remote-sensing image classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 37, no.3, pp. 1350-1359, May 1999.

t2 image

Bayesian decision rule for compound classification:

Number of classes

Joint posterior probability

1, 2, 1, 2,,

, , if , arg max ( , , ) i k

j j m n m n i k j jv N

x x v v P v x x

1, 2,( , , )j j i kp x x v ( , )i kP v

Map of land-cover transitions



Proposed Method: Compound Classifier

9

Assumption: class-conditional independence in the time domain

1, 2, 1, 2,,

, , if , arg max ( ) ( ) ( , ) i k

j j m n m n j i j k i kv N

x x v v p x p x v P v


Joint prior probability

Joint class-conditional density

Joint prior probabilities of land-cover transitions can be estimated on the basis of the expectation-maximization (EM) algorithm:

1, 2,

1 1, 2,

( ) ( ) ( , )1( , )

( ) ( ) ( , )s r

Bj i j k i k

k i kj j s j r s r

v N

p x p x v P vP v

B p x p x v P v

Image size

1, 2, 1, 2,( , , ) ( ) ( )j j i k j i j kp x x v p x p x v

L. Bruzzone, D. Fernandez Prieto, and S.B. Serpico, “A neural-statistical approach to multitemporal and multisource remote-sensing image classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 37, no.3, pp. 1350-1359, May 1999.




X1

X2

t1 image

Active Learning

Compound Classifier







X1

X2

t1 imageJoint

EntropyEstimation of

Statistical Distributions

t2 imageUncertain Samples Selection

Update Training Set

Training Set

Proposed AL Procedure


1,( )j ip x 2,( )j kp x v

( , )i kP v


Classconditional densities

Proposed Method: Active Learning

Joint Entropy

No YesConvergence?

1, 2,,j jH x xJoint prior probability


We propose to use the joint entropy to measure the uncertainty:

If joint entropy is small, the corresponding pair of pixels will be classified with high confidence, i.e., the decision on compound classification of these samples is reliable.

If joint entropy is high, the decision is not reliable, and therefore the corresponding pair of samples is considered as uncertain and critical for the classifier.

1, 2, 1, 2, 1, 2,( , ) ( , , ) log ( , , )i k

j j i k j j i k j jv

H x x P v x x P v x x

Joint entropy Joint posterior probability



We adopted two possible simplifying assumptions that result in two different algorithms of the proposed AL technique:

1.Algorithm (JEAL) is defined under the assumption of class-conditional independence:


1, 2, 1, 2,

1, 2,

1, 2, 1, 2,

( | ) ( | ) ( , ) ( ) ( ) ( , )( , ) log

( | ) ( | ) ( , ) ( ) ( ) ( , )i

k s s

r r

j i j k i k j i j k i kj j

j s j r s r j s j r s rv N

v N v N

p x p x v P v p x p x v P vH x x

p x p x v P v p x p x v P v



Class conditional densities


2. Algorithm (JEALInd) is defined under the assumption of temporal independence:


1, 2, 1, 2,( , ) ( ) ( )j j j jH x x H x H x

1, 1, 1,( ) ( | ) log ( | )i

j i j i jH x P x P x

2, 2, 2,( ) ( | ) log ( | )

k

j k j k jv N

H x P v x P v x

Marginal entropies

1, 2, 1, 2,( , , ) ( ) ( )j j i k j i j kp x x v p x p x v ( , ) ( ) ( )i k i kP v P P v

the class-conditional independence the independence of a-priori class probabilities on the two images

1, 2,

1, 2, 1, 2,

1, 2, 1, 2,

,

( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )log

( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )i k

s r s r

j j

j i j k i k j i j k i k

v N j s j r s r j s j r s rv N v N

H x x

p x p x v P P v p x p x v P P v

p x p x v P P v p x p x v P P v

Joint prior probabilityClass conditional densities



1. Algorithm (JEAL) is defined under the assumption of class-conditional independence:

2. Algorithm (JEALInd) is defined under the assumption of temporal independence:


1, 2, 1, 2, 1, 2,( , ) ( ) ( ) ( , )j j j j j jH x x H x H x MI x x

1, 2, 1, 2, 1, 2,( , ) ( ) ( ) ( , )j j j j j jH x x H x H x MI x x

Mutual information


Experimental Setup


Two different multitemporal and multispectral data sets are used (one made up of very high resolution images and one made up of medium resolution images).

Class conditional densities are estimated from the available initial training set assuming Gaussian distribution.

Results achieved with the proposed method are compared with Standard Marginal-Entropy based AL technique applied to the post classification comparison rule ignoring temporal dependence (Fully Independent).


Data Set Description

17

Multitemporal data set: Two images acquired by the TM sensor of Landsat-5 satellite in September 1995 and July 1996 (Lake Mulargia, Sardinia Island, Italy).

Land–cover classes: Pasture, Forest, Urban Area, Water, Vineyard S

epte

mbe

r 19

95Ju

ly 1

996


Data Set Number of Samples

Pool 2249

Test 1949


Experimental Results


Proposed 1=Proposed JEAL method defined under the assumption of class-conditional independence. Proposed 2=Proposed JEAL method defined under the assumption of temporal independence.


Experimental Results


Proposed 1=Proposed JEAL method defined under the assumption of class-conditional independence. Fully Independent=Standard Marginal-Entropy based AL technique applied to the post classification comparison rule ignoring temporal dependence.


Conclusion

20

A novel AL method has been defined on the basis of joint entropy defined in the context of compound classification for the detection of land-cover transitions.

Two different joint entropy based AL algorithms are implemented under two possible simplifying assumptions: i) the class-conditional independence; and ii) the temporal independence between multitemporal images.

Experiments show that the proposed joint entropy based AL technique, which takes advantage of temporal correlation, gives higher accuracies in detection of transitions.

Proposed AL method decreases significantly the cost and effort required for multitemporal reference

data collection; achieves high accuracy with a minimum number of multitemporal reference

samples; improves the performance of the standard marginal entropy based active learning

method by exploiting temporal dependence between images.



Future Development

21

Extend the proposed active learning algorithms

by including a diversity criterion defined in the context of compound classification.

considering label acquisition costs, which depend on locations and accessibility of the visited points for labeling the uncertain samples.


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