LSGENSYS - AN INTEGRATED SYSTEM FOR PATTERN

RECOGNITION AND SUMMARISATION OF MULTI-BAND

SATELLITE IMAGES

Hema Nair

C.T.R.F., 813, 7

Main, 1

Cross, HAL 2

Stage, Bangalore 560008, India

Keywords: Data mining, pattern recognition, image analysis, feature extraction, blackboard component, linguistic

summary, intelligent system.

Abstract: This paper presents a new system developed in Java

for pattern recognition and pattern summarisation in

multi-band (RGB) satellite images. Patterns such as land, island, water body, river, fire in remote-sensed

images are extracted and summarised in linguistic terms using fuzzy sets. Some elements of supervised

classification are introduced in the system to assist in the development of linguistic summaries. Results of

testing the system to analyse and summarise patterns in SPOT MS images and LANDSAT images are also

discussed.

1 INTRODUCTION

The processes and techniques that comprise data

mining, analyse raw data to discover implicit

patterns that are useful for decision-making. Pattern

recognition is another form of data mining because

both concentrate on the extraction of information or

relationships from data. A particular data mining

technique may be successful with one type of

multimedia such as images, but the same technique

may not be well suited to many other types of

multimedia due to varying structure and content.

A system that classifies and summarises patterns

such as land, island, water body, river, and fire, was

described in (Nair, 2004). The system utilised fuzzy

logic to describe these patterns. (Nair, 2006)

introduced some significant changes to the system.

A few of these changes have been implemented in

C.T.R.F.

’s

LSGENSYS. LSGENSYS (Linguistic

ummary Generation System) draws upon the

earlier techniques of utilising fuzzy logic, but also

adds a new significant element of user interaction

via the blackboard architecture component

(described in Section 2). This paper is organised as

follows. Section 2 explains the architecture and

design of the system. Section 3 explains the

methodology and approach. Section 4 presents

results of testing the system for image analysis,

pattern recognition and summarisation on

LANDSAT and SPOT MS satellite images. Section

5 presents the conclusions and future work.

2 SYSTEM ARCHITECTURE

LSGENSYS is an integrated system that allows the

user to analyse images, extract feature descriptors

such as area, length, location etc of patterns and then

use these descriptors to form linguistic summaries of

these patterns. This system also provides an

interactive environment, wherein, the user may

suggest some possible linguistic summaries for the

image patterns. The system would evaluate the

fitness of these user-summaries. Alternately, the

system could, without user intervention, generate

some possible summaries and evaluate their fitness

and suitability with respect to the image patterns.

The system architecture is shown in Figure 1. The

input image is analysed and some feature descriptors

extracted. These descriptors are stored thereafter in a

relational table in the database. The blackboard

holds the current state in the process of developing

summaries. This paper presents the system with the

significant change where, presently, the user has the

choice of suggesting concepts such as descriptions

of area, length, location of patterns etc. The

knowledge base uses geographic facts to define

feature descriptors using fuzzy sets. It interacts with

a built-in library of linguistic labels, which also

478

Nair H. (2007).

LSGENSYS - AN INTEGRATED SYSTEM FOR PATTERN RECOGNITION AND SUMMARISATION OF MULTI-BAND SATELLITE IMAGES.

In Proceedings of the Ninth International Conference on Enterprise Information Systems - AIDSS, pages 478-482

DOI: 10.5220/0002345204780482

 SciTePress

interacts with the summariser as it supplies the

necessary labels to it. The summariser receives input

from these components and performs a comparison

between actual feature descriptors of the image

patterns stored in the database, the concepts

suggested by the user, and the feature definitions

stored in the knowledge base. After this comparison,

the summariser uses the linguistic labels supplied by

the library to formulate some possible summaries for

each pattern/object in the database. These summaries

are stored in the blackboard. From among these

summaries, the most suitable one describing each

pattern is selected by interaction with the engine

(genetic algorithm). As the GA evolves through

several generations, it generates better summaries

(indicated by higher fitness, as defined in Section 4)

which are then stored and indicated on the

blackboard. Thus, the system has been improved and

enhanced to include some elements of supervised

classification and summarisation. The classification

rules that classify land, island, water body, river, and

fire are the same as in (Nair, 2006).

3 APPROACH

As in (Nair, 2006), area, length, location (X, Y pixel

co-ordinates of centroid of pattern in image),

Additional Information or Pattern Id, grey level

intensity, are the attributes of the patterns/objects

that are used to develop their linguistic summaries.

These attributes are calculated/extracted

automatically by the GUI tool. The linguistic

summary of patterns/objects is evaluated as follows.

p21

y,...y,yY = (1)

then

truth(y

isF)=μ

):i=1,2,...,p, (2)

where μ

) is the degree of membership of y

in the fuzzy set F and 0 ≤ μ

) ≤ 1. The linguistic

proposition y

is F could be instantiated as for

example, River is relatively long.

In order to generate such summaries, it is

necessary to formulate fuzzy sets that quantify

area/length attributes of the object/pattern.

Triangular and trapezoidal fuzzy sets (totally twenty

nine sets) have been formulated. The linguistic

description is calculated as follows:

njj2j1j

m...mmT ∧∧=

, (3)

where m

is the matching degree (Kacprzyk,

Ziolkowski, 1986) of the ith attribute in the jth tuple.

∈[0,1] is a measure of degree of membership of

the ith attribute value in a fuzzy set denoted by a

fuzzy label. The logical AND (∧) of matching

degrees is calculated as the minimum of the

matching degrees (Kacprzyk, Ziolkowski, 1986).

(4)

T in equation (4) is a numeric value that

represents the truth of a possible set of summaries of

the k objects in the database. The next section

discusses how the GA evolves the most suitable

linguistic summary for all the objects by maximising

4 IMPLEMENTATION ISSUES

This section explains the implementation of the

system, including the genetic algorithm approach

and then discusses the results from applying this

approach to analysing images.

4.1 GA Approach

Biological evolutionary theories are emulated by the

genetic algorithm (GA) as it attempts to solve

optimisation problems (Filho et al., 1994),

(Goodman, 1996), (Smith et al., 1994). Each binary

chromosome string in a population represents a

possible linguistic summary for a pattern. Such a

population of strings is manipulated by selection,

cross-over and mutation operators in the GA (Filho

et al., 1994) such that as the GA evolves through

several generations, only those strings with highest

fitness survive. The evaluation or fitness function for

Figure 1: System architecture.

LSGENSYS - AN INTEGRATED SYSTEM FOR PATTERN RECOGNITION AND SUMMARISATION OF

MULTI-BAND SATELLITE IMAGES

479

the linguistic summaries or descriptions of all

objects in the table is

f=max(T), (5)

where T is evaluated as shown in the previous

section.

4.2 Results and Discussion

Image objects/patterns are classified at the highest

level into land, water or fire. Land is further

classified into island and other land. Water is further

classified into river and other water body. The fuzzy

sets that quantify area or length are defined with

reference to geographic facts such as:

• Largest continent is Asia with area of

44579000 km

• Largest freshwater lake is Lake Superior

with area of 82103 km

• Smallest continent is Australia/Oceania

with area of 7687000 km

In (6), the triangular fuzzy set for considerably

large expanse of water is shown.

waterof expanse largely considerab

(x)=

1-(55068.66-x)/27034.33, for

28034.33≤x≤55068.66

=1-(x-55068.66)/27034.33, for 55068.66≤x≤

82103

=0, x< 28034.33

=0, x> 82103 (6)

An example SPOT MS satellite image to be

analysed is shown in Figure 2. Figure 2 shows the

image analysis tool of the system as it extracts area

and length attributes of three patterns in the image.

These attributes are calculated and displayed in the

data table in the figure. Pattern id attribute denotes

numbers as follows: 0= River, 1=Water Body,

2=Island, 3=Land, 4=Fire. Location is indicated by

X, Y pixel co-ordinates of centroid of pattern/object.

For river, its length is the most significant attribute

for calculation, whereas for all other patterns, area is

the most significant attribute for calculation.

The user may choose, at this stage, to interact with

the system and suggest some possible summaries.

The system can evaluate the fitness of the user-

summaries and inform the user if they are most

suitable to describe the image patterns in the table.

After the user chooses triangular fuzzy sets in the

knowledge base, the system displays triangular

fuzzy set definitions (lower-left window in Figure

3) in order to guide the user to select the appropriate

fuzzy set for area or for length of different patterns

such as land, island, water body, river etc. With the

aid of these fuzzy set definitions, the user then

constructs some possible pattern summaries as

shown in Window 1. The fitness value (1.0) of these

possible summaries is calculated and displayed in

Window 1. Thereafter, the user may verify the

correctness of these summaries and their suitability

with respect to the image patterns by running the GA

Inference Engine.

Figure 4 shows a snapshot of the GA Inference

Engine as it runs (in the top left window) and

evolves the most suitable summaries with higher

fitness, over several generations.

Figure 2: The image analysis tool of the system as it

extracts area and length of patterns in a SPOT MS image.

Approximate scale of image 1: 0.000019.

The summaries generated by the engine are

captured in a text file as displayed in the top right

window of Figure 4. The user may also chose to

bypass the summary construction process, and

directly invoke the inference engine (by pressing the

Run button in Figure 3) to construct and generate the

most suitable summaries. This would resemble

unsupervised classification and description.

The GA is run with following input parameter

set. These parameter values are set after several trial

runs.

No: of bits in a chromosome string of the

population = 10

Generations per cycle = 27

Population size = 200 strings

Probability of cross-over = 0.535

Probability of mutation = 0.001

After 216 generations, the linguistic summaries

generated for the data in Table 1 are:

• A short river at the top left

• A small area of land at the top left

• A small area of land at the right

Thus, the possible summaries formulated by the

user compare well with the summaries generated by

the inference engine.

ICEIS 2007 - International Conference on Enterprise Information Systems

480

Figure 5 and Figure 6 show the same procedure

as applied to a LANDSAT image. Table 2 shows the

data calculated from the image patterns. In Figure 5,

the system evaluates and displays the fitness of the

summaries formulated by the user. In this case, the

fitness value calculated is 0.778, which is considered

by the system to be too low. Therefore, the system

rejects the summaries and the user re-formulates

some possible summaries as shown in Figure 6. The

fitness of this set of summaries is higher at 0.888,

and the system accepts this possible set of user

summaries.

The GA Inference Engine is invoked by the user

to verify and validate the suitability of the possible

set of summaries. The resulting summaries from the

inference engine are enumerated below. The GA is

run with the same input parameter set as before, with

the exception that cross-over probability is set to

0.538.

After 216 generations, the linguistic summaries

generated for the data in Table 2 are:

• A small area of land at the top right

• A small area of land in the lower part

• A moderately large expanse of water at

the centre.

These summaries match the user-formulated

summaries in Figure 6.

The blackboard architecture component of the

system has been implemented via the user

interaction process. The sequence of windows such

as Fuzzy Set Type selection window, Fuzzy Set

Definitions window, Window 1, Run Inference

Engine Window, and the Outpt window are

programs integral to the blackboard architecture

component. The Data Table and possible set of user-

formulated summaries in Window 1 are part of the

blackboard data structure. Control in the blackboard

architecture directs the order in which the programs

are invoked depending on the user-formulated

summaries stored in the blackboard.

Table 1: Data calculated from image in Figure 2.

Figure 4: Snapshot of the GA Inference Engine as it runs

and evolves the most suitable summaries that fit all the

patterns extracted from the image.

5 CONCLUSIONS AND FUTURE

WORK

This paper has presented a new system developed in

Java for image analysis and pattern recognition in

multi-band satellite images.

Table 2: Data calculated from image in Figure 5.

The system architecture and design have been

described. The system has been tested successfully

with a SPOT MS image and a LANDSAT image and

the results have been presented and discussed. Some

directions of future work include: adding the

provision to upload ground data in order to help

classify more patterns such as vegetation in satellite

images using supervised classification techniques,

adding enhancements to image analysis functions,

adding a scripting feature that allows the user to

program a sequence of image analysis instructions in

a user-friendly language.

R-band grey

level

Approx

Area

Approx

Length

Y Id

2 - 1.772 49 87

149 1.1186 - 185 190

116 0.1775 - 24 33

R-band

grey

level

Approx

Area

sq km

5 18979.83 88 71 1

91 2565.61 135 10 3

36 2317.14 97 133 3

Figure 3: The user icon on the second horizontal menu bar

is clicked in order to start the user interaction process.

LSGENSYS - AN INTEGRATED SYSTEM FOR PATTERN RECOGNITION AND SUMMARISATION OF

MULTI-BAND SATELLITE IMAGES

481

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Figure 5: A LANDSAT image is analysed by the system

and some possible summaries formulated by the user.

Approximate scale of image is 1: 0.952 sq km.

Figure 6: For the image in Figure 5, a different set o

summaries as formulated by the user and fitness re-

evaluated by the system.

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482