Fuzzy Semantic Retrieval of Distributed Remote Sensing Images

Heng Sun, Shixian Li, Wenjun LiDepartment of Computer Science

Sun Yat-Sen UniversityGuangzhou 510275 China

ispO3 sh@taurus. sysu. edu.cn

Abstract

Because of the surprisingly increasing volume andsemantically fuzzy nature of remote sensing images(RSIs), one of the main obstacles to realize efficientretrieval of the RSIs is the lack of effective sharingtechnologies and semantic description methods. In thispaper, we present a fuzzy ontology and implement aprototype grid system named RSIsFGridfor semantic-based RSIs retrieval using fuzzy ontology and gridtechnologies. In order to verify this method, measuressuch as the recall and precision are used Test resultshave indicated that the fuzzy ontology-based methodcan promote the query performance ofRSIs.

1. Introduction

During the last decades, the imaging satellitesensors have acquired huge quantities of data. Thestate-of-the-art systems for accessing remote sensingdata and images, in particular, allow only queries bygeographical coordinates, time of acquisition, andsensor type [1]. This information is often less relevantthan the content of the image, e.g. structures, patterns,objects, or scattering properties. Thus, only few of theacquired images can actually be used [2]. In the future,the access to distributed image archives will evenbecome more difficult due to the enormous dataquantity and distributed information sources acquiredby a new generation of high-resolution satellitesensors. As a consequence, new technologies areneeded to easily and selectively access the informationcontent of image archives and finally to increase theactual exploitation of satellite observations [3].

This work was supported in part by the Natural Science Foundationof Guangdong under Grant No. 06017089, in part by the Ph.D.Programs Foundation of Ministry of Education of China under GrantNo. 20030558004, and in part by the National Natural ScienceFoundation of China under Grant No. 60373084.

Xiaoyong MeiGuangdong Lingnan Institute of Technology

Sun Yat-Sen UniversityGuangzhou 510663 China

semantic grid@hotmail.com

The application of knowledge mining technology toremote sensing images (RSIs) has recently gainedmomentum [4][5][6], and an initiative in this directionis ontology-based information retrieval which can beconducted to some extent at the abstract semantic level.An ontology is a formal conceptualization of a realworld, and it can share a common understanding of thisreal world [7]. With the support of the ontology, bothuser and system can communicate with each other bythe shared and common understanding of remotesensing domain [8].

In the process of ontology-based image featuresextraction, the annotator plays an import role [9].However, exploitation of the human analyzingcapabilities has disadvantages, partly because it istedious and time consuming, and partly because itsuffers of subjectivity and partiality, since differentannotators will often consider different aspects of thecontent as important, and an exhaustive annotation isconsidered unrealistic. Another difficulty arises fromthe multitude of relations defined in the ontology.While these enable the annotator to provide asufficiently accurate description, they present aproblem to the user, who usually desires simple formsof queries.

Fuzzy set theory [10] provides useful concepts andtools to deal with imprecise information. We havefaced the above problems with the aid of fuzzy settheory, introducing the concept of the fuzzy ontology.The fuzzy ontology is an extension of the domainontology that is more suitable to describe the domainknowledge for solving the uncertainty reasoningproblems. By using the fuzzy ontology, the user querycan be expanded to contain all the associated semanticconcepts. The expanded query is expected to retrievemore relevant images, because of the higherprobability that the annotator has included one of theassociated concepts in the description.

In this paper, we present a principle for storingsemantic instances of the RSIs and implementing RSIs

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data retrieval by fuzzy inference. Moreover, theresearch outlines our experience of building asemantically rich system which goal is to facilitatecomplex and fuzzy query processing over distributedRSIs archives. This system, which is named as theRSIsFGrid, utilizes grid technology [11] to enablesharing and selection of the RSIs distributed overdifferent locations. It is part of GIS Grid ServicesProject which supports not only integrating GIS dataand services as web services, but also sharing dynamicresources, such as high-performance computers, high-capacity storage resources, and heterogeneous sensors[12].

2. RSIs fuzzy ontology

In this section, we extend previous work [13] andfurther present a fuzzy ontology for RSIs retrieval.Because the fuzzy ontology is an extension of thedomain ontology, we briefly introduce the domainontology. Now, we give a formal definition of thedomain ontology for RSIs retrieval.

[Definition 1] Domain Ontology: A domainontology defines a set of representational terms that wecall concepts. Relations among these concepts describea target world. This paper has adopted a formaldescription method [14] for the RSIs ontology.According to the chosen formalism, the RSIs ontologyis defined as (Figure 1) 0 = (C, P, D, R, S):C is a set of RSIs classes, such as Time, Sensor,

SatelliteImage, etc. Each object of a class is consideredas an instance of the class. P is a set of specificproperties that characterize these classes and propertiesare usually represented with plain literals as propertyvalues. D is a set of property domains and this paperfocuses our attention on the RSIs domain. R is a set ofrelations, including equivalence(Re), generalization(Rg),aggregation(Ra) and association(Rs), in the domainontology. S is a set of constraints. For chosen notations,the following five types of constraints have beendefined: S = SI U SI, U SI,, U SIV U Sv

* SI {S, SI = (C, p), 3reRR.domain(r)=C A

range(r)=p, pEP, c E C }, representing the constraint"properties to classes";

SII {SIII, SI = (p, d), peP, deD}, representingthe constraint "properties to domains";

* SI,, {S,,, sII-r({c})e Boolean, re {equivalence,generalization, aggregation, association}, {c} 1>2,cc C}, representing the constraint "relations among theclasses";

equivalenat / ,RISPhoto Timenw(SIj R, constrainte Superclass1,,4 RSlmage Sensor

part(' S, (5( :<'trasituive)</ < f< </e/ \ ~~~~~~(Sill: R,,con}s.trinZt.) I'

oX ---jv -.zfIoCbus1fn .Ey\ ScanCut nX) , Xf <'LiAerialPhoto \ TiunieXf RISs,<> 5Z1 ,~~~~-

I Resolution IDomain SubclassProperty--- cp-e n

Satellutellmuage Satellite

Figure 1. Constraint network of RSIs ontology

* SIv {SIV siv-cardinality(r)>O, pe P},representing the constraint "cardinality";

* Sv {sv sv=g(r) e Boolean, ge {transitive,symmetric, reflexive}, re R}, representing theconstraint "relation characteristics".

In this section, we extend the domain ontology to bethe fuzzy ontology by embedding a set of membershipdegrees in each relation of the RSIs ontology. Therelation with the membership degrees is called fuzzyrelation. Now we give the definitions of fuzzy relationand fuzzy ontology as follows.

[Definition 2] Fuzzy Relation: A fuzzy relation is arefined relation derived from a domain ontology. It is arefinement by embedding a set of membership degreesassociated with a set of the relations among theconcepts of the domain ontology.

It is well understood that relations among RSIsconcepts are always a matter of degree, and are,therefore, best modeled using fuzzy relations.Ontological vocabulary, on the other hand, is crisp inprinciple. Thus, it fails to fully describe RSIs concepts,and is limited to 7-cuts of the desired relations [10]. Inorder to explain the difference, consider A as a fuzzysubset of X, where X is a space of relations or what iscalled a "Universe of Discourse". Fuzzy set A in X ischaracterized by a membership function flA(X) or ,u(x)which associates a real number in the interval [0,1]with each relation x in X. Then it's 7-cut A) is a non-fuzzy subset of X defined by Ak={x ,(x) >X}. This isa very important drawback, that makes such relationsinsufficient for the services that an RSIs retrievalsystem aims to offer.

In order to overcome such problem, fuzzy semanticrelations are proposed for the modeling of RSIsresources. We refine an relation rij between concept ciand cj into a fuzzy relation and denote the fuzzyrelation as [rij, fij], where uij represents the membershipdegree of rij for ci and cj. A few commonly encounteredsemantic relations are modeled as fuzzy relations, andtheir combination is proposed for the generation of ameaningful, fuzzy relation.

The generalization relation [rg, pig] is a fuzzy partialordering relation on the set of semantic concept. fug(ci,Cj)>O means that the meaning of ci "includes" the

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@:CloudCover R_(dependsOn, /) CTyphoonX QualityC Lineage Ae p

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C Horizontal = KNorthWestPointLongC Orientation ..L.,. (rdf:datatype:decimal),canTime P SouthEastPointLat

(rdfdatatype:decimal)P Durationa PSouthEastPointLong

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alization association C: Concept tegation equvalence hasproperty 9

Figure 2. A subset of the RSIs fuzzy ontologymeaning of cj and the most common form ofgeneralization is subClassOf. For example ci could bean RS image and cj could be a satellite image. The roleof the generalization relation in semantic-basedretrieval is as follows: if an image refers to themeaning of concept cj, it is also related to ci, since cj isa special case of ci. The aggregation relation [ra, [Ia] isalso a fuzzy partial ordering on the set of semanticconcept. pa(ci, cj)>O means that cj is a part of ci. Forexample ci could be weather and cj could be typhoon.The role of ra in semantic-based retrieval is as follows:if the user query contains cj, an image containing ciwill probably be of interest, because ci contains a partcj. By using the equivalence relation [re, je], the user

query is expanded to contain synonymous concepts.For example ci could be an image and cj could be a

photo. Finally, as for the association relation [ri, u,],p#(c1, cj) >0 implies that concepts ci and cj are related,however loosely. This kind of relations, such as

dependsOn, will give concepts that are less stronglyrelated, than the other three.

In this paper, fuzziness of the aforementionedrelations has the following meaning: High values fug(ci,cj), imply that the meaning of cj approaches themeaning of ci, in the sense that when an image isrelated to cj, then it is most probably related to ci as

well. Likewise, the degrees of the other relations can

also be interpreted as degrees of implied relevance.[Definition 3] Fuzzy Ontology: A fuzzy ontology is

an extended domain ontology with fuzzy relations.Figure 2 illustrates the RSIs fuzzy ontology.

Considering the enormous size of the ontology, thisfigure only contains part of classes, properties,

constraints, and semantic relations. To provide an

extensible model, this ontology mainly employs theRSIs metadata standard, Content Standardfor DigitalGeospatial Metadata. Extension for Remote SensingMetadata [15], as concept vocabulary of the RSIsdomain. The whole vocabulary now includesthousands of terms and we have also augmented some

lexical items, such as Vegetation, Mountain, andWaterBodcy, etc., to meet semantic inference among theconcepts.

3. RSIs retrieval system

3.1. RSIsFGrid overview

RSIs retrieval system is deployed on Linux Redhat9.0 and requires Axis application server with a

database backend, PostgreSQL 7.3.2, for persistentstorage. As an open source software toolkit used forbuilding grids, the Globus Toolkit (GT) provides a

distributed environment and a set of components thatimplement basic services, such as security resource

location, resource management, and communication,etc. Thus, we use GT4.0.0 to build the system in orderto retrieve the distributed RSIs [16]. The hardwareplatform consists of 5 nodes and 1 client. All the nodesare equipped with Athlon XP 2200+ processors,running at 1.8GHz, and 256MB memory. Anotherclient has 256MB memory with a CPU frequency ofAthlon XP 2000+ 1.667GHz. They are connected with100Mbps Ethernet.

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Distriibuted >RSIs wih ih

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Figure 3. The RSIsFGrid Architecture

The architecture of the RSIsFGrid is shown inFigure 3:

1) Semantic Annotation Node: The RSIs fuzzyontology, expressed in terms of the RDF schema [17]together with fuzzy relations, provides an aid toannotate semantic information of the distributed RSIs.

2) RDF Index Node: The RDF instances ofannotated images are then classified into clusters forlater retrieval by automatic indexing based on the RDFtriple descriptions of the RSIs. As the indexing is basedon the grid environment, the distributed RSIs resourcescan dynamically participate in the existing RSIsFGridand the obsolete RSIs can also be discarded relying onsoft state mechanism provided by the GT.

3) RSIsFGrid Portal Node: The portal providesdiverse query modes to retrieve query information thatusers input. The user query can be composed of eitherdirect references to the semantic concepts and relations,or of words. In the latter case, the user's request mustbe transformed to a query containing semanticconcepts. The portal can expand query conceptsaccording to the fuzzy relations in the ontology. Thefuzzy expansion provides support for storing andtraversing crisp classes and fuzzy relations. Thisimplements the fuzzy transitive, reflexive, and(or)symmetric relations of rdfs. subClassOf isPartOf,dependsOn, and equivalentClass, etc. The knowledgefusion engine in this node adopts the Flat FusionPattern [18] to process the distributed annotationinformation of the RSIs and can match expandedconcept description with the indexed image clusters tofind the most similar image. The access service is adata service for accessing and integrating dataresources, such as files, relational and XML databases.

4) Network Prediction Node. The Forecast Serviceis a distributed service that periodically monitors anddynamically forecasts the performances of various

Quality(dependsOn

Resolution

(CSemanticlinfo ( Country(rdf string)RSlrae\ Ciyw

DRSImS][rtWa:ge 3 (rdfstring)Quality

E ellentI(d)pmldepiOni(8)

C)Semanticlnfo TGD tQuality}nfo0et20030204

-. 0.60(m)CResolution

0 RSI eIita&c)srcktons(IDI)

Figure 4. Annotation of a satellite image

networks and computational resources to optimum theRSIs retrieval.

5) Transfer Node. After obtaining the result images,The Reliable File Transfer Service can call theGridFTP toolkit provided by the GT and reliablytransfer the result images to the users. If the transferfails, the stored transfer state is sufficient to resume orrestart the interrupted transfer.

Since grid services are transient, the RSIsFGrid cancreate on-demand service instances and provideQuality of Service (QoS) support.

3.2. Semantic annotation and index structure

The idea of semantic annotation is to assign domainconcepts in terms of semantic tags that are well definedin the ontology to the phrases in the descriptivemetadata so that it could facilitate the retrieval of theRSIs based on the semantic tags. The text contentdelimited by these tags is manually inputted from theoriginal metadata values in order to facilitate thegeneration of annotated descriptive results inaccordance with the RDF standards.

When introducing new instances, it is notmandatory to instantiate the exact concept. Aninferential engine can "fine tune" the hierarchicalstructure based on the concept and instance definitions.For instance, an individual image TGD_Oct20030204can be specified initially to concept "RSImage"(Figure 4). With proper values assign to its properties,an inferential engine can re-classify and fine-tuneTGD_Oct20030204 to the most suitable location in theconceptual hierarchies (e.g. TGD_Oct20030204 ESatelliteImage).

To retrieve the RSIs from image database can bevery time-consuming and inefficient if the images arenot properly indexed. Since each image has beenannotated in terms of the RDF instance, we build

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Table 1. RSIs instances indexSubject/Class instance Predicate/Relation Object/Property Class... /TDG_Oct20030204 Country, 1 China Satellitehmage... /TDG_Oct20030204 City, 1 Yichang Satellitehnage/TDG Oct20030204 0,1 Yichang China Satellitehnage

automatic indexing engine according to the tripledescriptions of RSIs resources.

As shown in Figure 4, we assume that URLTDG isthe storage address of an image and write this in thetriples notation as follows:

[URLTDG, (rdf:type, 1), SatelliteImage][URLTDG, (Country, 1), "China"][URLTDG, (City,1), "Yichang" ]

We create a multidimensional index by combiningsuch triples as follows:

[URLTDG, (Country, 1), "China", SatelliteImage][URLTDG, (City, 1), "Yichang", SatelliteImage]

To represent the fact that the phrase "YichangChina" is the location value that the image covers, wecreate a further quadruple:

[URLTDG, (0,1), "Yichang China", SatelliteImage]The obtained quadruples are then stored in the

image replicas database along with references to theirstructural origin. Table 1 schematically shows the datastructure used to store the index.

3.3. Fuzzy inference mechanism for queryexpansion

The image replicas database contains a set of imagedocuments, and their respective descriptions. In asemantic query, the user asks for specific semanticconcepts and the system returns images whosedescription semantically contains them.

It is certain, however, that the annotator has notincluded every possible semantic concept in thedescription. For example, in the above ontology, it ismentioned that an aerial photo is an RS image, and thatan RS image is an image, but it is not mentioned thatan aerial photo is an image. Therefore, a queryrequesting images will not retrieve an event involvingaerial photo. On the other hand, inclusion of everypossible semantic relation would be both non-realisticand redundant.

The fuzzy inference would be used as a solution tothis problem. Using the semantic fuzzy relations, it ispossible to find, for each semantic concept of the query,the set of its related concepts, and expand the querywith them. In our example, the query concept "Image"would be expanded to "RSImage".

Let C, D and E denote three crisp concept sets,which, for our application, we assume to be finite, i.e.C={c1, ..., cl}, D={di, ..., dm} and E={e1, ..., e}. A

fuzzy binary relation is defined as a function R:CxD-÷[O, 1]. It is often convenient to represent abinary relation as a matrix:R = [u1] = [R(rij)] = [R(c1, cj)] 0 < i < 1, 0 <j < m . (1)

Given two fuzzy binary relations R and Q, definedon CxD and DxE, respectively, their composition sup-T is defined:

R°Q [ sup T(ri., qk)] 0<i<1,0<k<n. (2)O<j<m

For relations defined on a single set, i.e. R:CxC->[O, 1], the properties of reflexivity, symmetryand sup-T transitivity are defined:

* R is reflexive ifR(c,c)=1 for all ce C;

* R is symmetric ifR(c,d)=R(d,c) for all c,de C;

* R is antisymmetric if R(c,d)>0 and R(d,c) >0imply that c=d for all c,de C;

* R is sup-T transitive ifR°R c R.A transitive closure of a relation is a transitive

relation that contains the original relation and has thefewest possible members. It is given by the formula RTr= R u R(2) u... UR(n) where n denotes the numbers ofconcepts and R(n)= R(n-1)-R. Examples of properties arefound in Table 2.

These properties arise from the relations alone, anddo not depend on the specific concepts related.However, data supplied by the annotator does notalways satisfy those properties. For example, thedependsOn relation is transitive. While the annotatormight have stated that "the price of an RSI" dependson "its quality" and "the quality of an RSI" depends on"its resolution", it is not certain that he or she has alsostated that "the price of an RSI" depends on "itsresolution". A transitive closure would correct thisinconsistency. Other closures on relations are similar.

Table 2. Properties of some semantic relationsRelation Properties

equivalentTo reflexive, symmetric, transitivedependsOn transitivesubClassOf reflexive, antisymmetric, transitivesimilarTo symmetric

3.4. Knowledge fusion mechanism

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TD .Oct2OO3O2040,

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Qualityirto tSemailnfbtiQualityinffSemanti

(oResolution CQdity City; Arema Resoton Qtuity Contry y:, e2Ychang Sandouping e China Yichang0 60m Excellent0i.60m Excellent

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VResolution = Qality Country: City: Area:drL ty

China Yichai g Sandouping0 60m Excellent

Figure 5. Flat fusion pattern of the RSI instances

Table 3. The experiment results of RSIs retrievalONTOLOGY-BASED METHOD

ci 70 67 67 38 32 34 30 21 31 26ni 20 11 4 25 25 16 14 15 2 5fi 34 39 44 10 4 13 12 3 20 14

Recall (%) 77.8 85.9 94.4 60.3 56.1 68.0 68.2 58.3 93.9 83.9Precision (%) 67.3 63.2 60.4 79.2 88.9 72.3 71.4 87.5 60.8 65.0

Fuzzy ONTOLOGY-BASED METHODci 73 69 67 42 35 36 32 23 31 26ni 17 9 4 21 22 14 12 13 2 5fi 29 45 43 11 3 12 13 4 20 14

Recall (%) 81.1 88.5 94.4 66.7 61.4 72.0 72.7 63.9 93.9 83.9Precision(%) 71.6 60.5 60.9 79.2 92.1 75.0 71.1 85.2 60.8 65.0

The descriptive metadata of an RSI, such asSemanticInfo and QualityInfo concept in Figure 2, maybe widely distributed over the RSIsFGrid since theannotators of an RSI may select many different kindsof feature metadata to annotate this image. We mustmerge these metadata in order to process the users'requests correctly. This kind of semantic fusion patterncan automatically implement the union of imagemetadata. Once the descriptive metadata are stored inRSIs semantic instances, we can merge these instancesinto one. The union of instances is actually the unionof the sets of semantic relations and the intersection ofrelation membership degrees. It is one of the keyoperations that fuzzy ontology technology supports. Asshown in Figure 5, the semantic fusion enables datafrom disparate images sources to be merged. Theinitial instances dissolve in the new semantic instanceand do not preserve their duplicate internal structures.

4. Experiments and results

The experiments were carried out on a set of 200RSIs of size 1024X768 and these images were takenfrom the QuickBird Satellite Imagery Library [19].This image library also provided metadata descriptions

of these images. Before the experiments, we manuallyconducted semantic annotations on these metadatadescriptions with the aid of the RSIs fuzzy ontology.Image clusters were grouped by pre-annotated foldernames. The RSIs archive was collected byDigitalGlobe, Inc. during a four-year interval from2002 to 2005 and contained 20 folders. These folderswere named with a brief line starting with the datewhen the RSIs were taken and following by a briefindication of the event, subject or location related tothe content. We tested the performance of theRSIsFGrid based on fuzzy ontology by comparing withour previous work on the ontology-based RSIsGrid. Inthe experiments, we selected 15 queries at random. Ofthe 15 queries, 5 valid ones did not yield any retrievedimages in both methods because no images among the200 tested met the query criteria. The experimentresults based on the remaining 10 queries are shown inTable 3.We use the standard measures, recall and precision,

to evaluate the results:ci

Recall = .± * I100%.Ci + nici

Precision =C * 100%.Cl +-fl

(3)

(4)

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where ci is the number of RSIs retrieved correctly, ni isthe number of true RSIs not retrieved, and fi is thenumber of false RSIs retrieved.We observe that the fuzzy ontology-based retrieval

excels the ontology-based one in both precision andrecall. The average precisions are 72.1% and 71.6%respectively. The average precision gap is 0.5%.Compared with it, the recall gap is 3.2%. It is easy tosee that, we adopt novel fuzzy semantic relations forexpanding the query with its associated terms andincrease the retrieval recall. Therefore, the fuzzyontology-based method can improve the queryperformance of the distributed RSIs.

5. Conclusions

In this paper, we have established the RSIsFGridsystem for facilitating RSIs retrieval using fuzzyontology and grid technologies. Once the metadata andattributes of the RSIs have been converted intoinstance elements with the aid of the fuzzy ontology,the RSIs can be matched and retrieved at an abstractsemantic level. And the RSIsFGrid system candynamically share a large volume of distributed RSIs.Test results have indicated that the fuzzy ontology-based method can promote the query performance ofRSIs by the fuzzy expansion of user query.

6. References

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[7] N. Lammari and E. Metais, "Building and MaintainingOntologies: A Set of Algorithm", Data Knowledge Eng., vol.48, pp. 155-176, 2004.

[8] S. S. Durbha, and R. L. King, "Semantics-EnabledFramework for Knowledge Discovery From EarthObservation Data Archives", IEEE Trans. Geosci. RemoteSens., vol. 43, no. 11, pp. 2563-2572, Nov. 2005.

[9] G. Votsis, A. Drosopoulos, G. Akrivas, V. Tzouvaras andY. Xirouhakis, "An MPEG-7 Compliant Integrated Systemfor Video Archiving, Characterization and Retrieval", InProc. IASTED International Conference on Signal andImage Processing, Las Vegas, November 2000.

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[14] A. Smimov and C. Chandra, "Ontology-BasedKnowledge Management for Co-operative Supply ChainConfiguration", In Proc. AAAI Spring Symp. BringingKnowledge to Business Process, 2000, pp. 85-92.

[15] Content Standard for Digital Geospatial Metadata:Extensions for Remote Sensing Metadata, the FederalGeographic Data Committee Standard FGDC-STD-012-2002[Online]. Available: http://www.fgdc.gov/standards.

[16] Globus Toolkit 4.0.0, Globus Alliance [Online],available: http://www.globus.org/toolkit/downloads/.

[17] RDF Semantics, W3C Recommendation [Online].Available: http://www.w3.org/TR/rdf-mt/.

[18] C.V. Holsapple, M. Singh, "The knowledge chain model:activities for competitiveness", Expert Syst. Appl., 20 (2001)77-98.

[19] Satellite Imagery Library, DigitalGlobe Inc., Longmont,CO, 2004.

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