High Level Adaptive Fusion Approach: Application to LandCover Change Prediction in Satellite Image Databases

Wadii BoulilaWadii.Boulila@

riadi.rnu.tnRIADI, ENSI, Tunisia

Karim S. Ettabaakarim.sahebettabaa@

riadi.rnu.tnRIADI, ENSI, Tunisia

Imed Riadh Farahriadh.farah@ensi.rnu.tn

RIADI, ENSI, Tunisia

Basel SolaimanBasel.Solaiman@

telecom-bretagne.euITI, Telecom-Bretagne,

France

ABSTRACTThe purpose of this paper is to propose an adaptive pos-sibility fusion approach to take into account the reliabilityof different decisions related to the prediction of land coverchange. The proposed approach reduces the influence of un-reliable information and thus enhances the relative weight ofreliable information. Decisions about changes are obtainedby applying previous works and represented as spatiotempo-ral trees. These trees are combined to obtain more accurateand complete ones. Validation of the approach shows goodperformances of the proposed approach in improving theprediction of land cover change.

Keywordsadaptive possibility fusion, land cover change prediction, de-cision fusion, satellite images.

1. INTRODUCTIONLand cover change prediction is an important subject in

global environmental change. Modeling land cover changehelps analyzing causes and consequences of land change inorder to support land cover planning and policy. However,imperfection related to the prediction of land cover changeaffects sometimes the accuracy of decisions.In previous works, the issue of land cover change predictionhas been discussed [1]. The proposed approach allows forbuilding spatiotemporal change trees describing land coverchange.However, each spatiotemporal change trees generated by theproposed approach depicts a partial view about changes fora particular land cover type. Combining these trees can helpimprove the prediction of land cover changes and decreasethe associated imperfections.In this paper, we investigate the issue of combining severalspatiotemporal change trees to provide more accurate deci-sions about land cover changes. Tree combination is ensuredby an adaptive possibility fusion method. Fusion is applied

Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and that copiesbear this notice and the full citation on the first page. To copy otherwise, torepublish, to post on servers or to redistribute to lists, requires prior specificpermission and/or a fee.SAC’12 March 26-30, 2012, Riva del Garda, Italy.Copyright 2012 ACM 978-1-4503-0857-1/12/03 ...$10.00.

Figure 1: Spatiotemporal change tree of obj betweent and t’

at the decision level and aims to integrate the reliability ofeach change tree in the fusion process.

2. PROPOSED APPROACHLet us suppose that a set of spatiotemporal change trees

are obtained by previous works [1]. Each tree provides a par-tial view about changes made throughout time for a givenobjectobj extracted from a satellite image between two datest and t’. A confidence degree conf is accorded to each tree toillustrate its reliability in describing changes of obj. Figure1 shows an example of spatiotemporal change trees of objbetween two dates t and t’.WhereC1, C2,. . ., Ck are the possible land cover types to which theobject obj can evolve.per1, per2 ,. . ., perk are respectively the percentages of changesof the state Sq to C1, C2,. . ., Ck.deg1, deg2, . . ., degk are respectively the confidence degreesof changes of the state Sq to C1, C2,. . ., Ck.In this paper, the possibility fusion method is used to com-bine decision provided by different change trees and there-fore obtain a global land cover change decision. Let D ={C1, C2,. . ., Cn} be the set of possible decisions; Cr (1 ≤r ≤ n) representing the possible land cover types to whichan object can evolve. The need of an adaptive fusion seemsto be necessary to take into account the reliability of eachchange tree.The main challenge is to determine confidence degrees andpercentages of changes for the query state Sq. m fuzzy setsare computed for the query state Sq as follow [2]:

{π1(Sq), π2(Sq), . . . , πi(Sq), . . . , πm(Sq)} (1)

πi(Sq) is the membership degree of a state Sq to class Craccording to the decision tree i. By applying the adaptivepossibility theory, we can combine confidence degrees (re-spectively percentages of changes) and therefore obtain aglobal decision about changes for obj.The adaptive combination rule used in this study is given

21

Table 1: Urban changes between 2002 and2009 after applying the fusion module(C1=lake,C2=vegetation, C3=bare soil, C4=forest and C5=urban

C1 C2 C3 C4 C5Proposedchanges

per 0.68 2.54 8.77 16.25 71.76deg 86.38 82.03 84.31 94.80 93.89

Table 2: Real changes of the urban site between2002 and 2009

C1 C2 C3 C4 C5Real urban changes 0.08 3.74 7.19 16.83 72.16

by:

πCrf = max(min(ωiπCri (Sq), f

Cri (Sq), 1 ≤ i ≤ m)) (2)

Where fCri is the global confidence of the change tree i forthe land cover type Cr. ωi is the normalization factor de-fined in equation (4). According to [2], this combination ruleensures that only reliable sources are taken into account foreach class. {

ωi =∑mk=1,k 6=iHαQE(πk)

(m−1)∑mk=1

HαQE(πk)∑mi=1 ωi = 1

(3)

where HαQE(πk) is the fuzziness degree of the spatiotem-poral change tree i,πk = {πCrk , r = 1 . . . n}. According to[2], α (α=0.5 ) is a selective parameter. It allows havingfuzzy sets with approximately the same degree of possibilityor with different degrees. The fuzziness degree HαQE(πk) isdefined as follows:{

HαQE(πk) = 1n

∑nr=1 SαQE(πCk (Sq))

SαQE(πCk (Sq)) =πCk (Sq)

α(1−πCk (Sq))α

2−2α

(4)

Decisions in the possibility approach are usually taken us-ing the maximum possibility degree.

3. VALIDATIONThe goal of the validation section is to evaluate the role of

the fusion module in improving the prediction of land coverchanges.Let us also suppose that 10 change trees describing urbanchanges between 2002 and 2009 are computed using previ-ous work [1]. Each tree describes a partial view about per-centages of urban changes and confidence degrees accordedto these changes associated with the following land covertypes: lake, vegetation, bare soil, forest and urban. A confi-dence degree conf is accorded to each tree. These trees arecombined in order to provide global urban changes (Table1).

In order to evaluate performances of the fusion process inpredicting urban changes, real changes based on an imagepresenting the same zone and acquired on March 21, 2009are computed.Table 2 depicts real changes of the urban site between 2002and 2009. The comparison between real changes, proposedchanges (after applying the adaptive fusion of the ten changetrees) and the tree estimating the best urban changes (CT1)depicts that the proposed urban changes are the closestchanges to the real ones. Error in predicting urban changes

Figure 2: Min, Mean and Max values of the errorrate in predicting land cover changes for the adap-tive fusion method (M2) and the tree estimating theclose urban changes (M1) for the 12 experiments

is equal to 4.36% for the proposed approach while it is equalto 5.6% for CT1.In order to better evaluate performances of the adaptivefusion method, 12 experiments are performed. Twelve dif-ferent periods are considered. Predicted land cover changesfor these 12 periods are estimated through the proposed ap-proach. Then, real urban changes are evaluated based onimages representing same dates in each period. The numberof occasions having the minimum of prediction error accord-ing to the 12 previous experiments can be discerned. Theadaptive fusion method has 9 occasions minimum of errorprediction for the water zone, 7 occasions for the forest zone,6 occasions for the bare soil zone and 9 occasions both thevegetation and urban zones.Figure 2 illustrates the minimum, mean and maximum val-ues of the error rate in predicting land cover changes forthe adaptive possibility method (M2) and the tree estimat-ing the close urban changes (M1) for the 12 experiments foreach land cover type.In most cases, fusion of the land coverchanges helps decreasing error related to land cover changeprediction.

4. CONCLUSIONLand cover change prediction is a challenging issue in the

remote sensing field. However, making decisions related toland cover changes requires accurate predicted changes. Inthis paper, an approach for combining several predicted landcover changes is proposed. The proposed approach is basedon an adaptive fusion method to take into account the relia-bility of partial land cover change decisions. It allows reduc-ing imperfections related to the process of land cover changeprediction. In order to evaluate the performances of the de-veloped system, we compared proposed urban changes toreal ones. Results show good performances of the proposedapproach.

5. REFERENCES[1] W. Boulila, I. R. Farah, K. S. Ettabaa, B. Solaiman,

and H. B. Ghezala. A data mining based approach topredict spatiotemporal changes in satellite images. Int.J. Appl. Earth Obs. Geoinf., 13(3):386–395, 2011.

[2] M. Fauvel, J. Chanussot, and J. A. Benediktsson.Decision fusion for the classification of urban remotesensing images. IEEE Trans. Geosci. Remote Sens.,44(10):2828–2838, 2006.

22