Land use discrimination based on textural characteristics

LAND USE DISCRIMINATION
BASED ON f EXTURAL CHAWCTERISTICS

Bambang Hendro Trisasongko

GRADUATE PROGRAM
BOGOR AGRICULTURAL UNIVERSITY

BOGOR
2002

' IQW' "
Simple but has extremely deep means

Kat ur:
Ibu' ,Bapak

Dyah tan Palupi

PREFACE

Environmental remote sensing is the measurement of earth featums from a
distance. Many sensors have been built to identify, monitor and model the
environmental condition. One of the largest applications of remote sensing is to
i d e n t i land use. Land use data is overwhelmingly distributed and utilized by
wide spread of users. In soil science domain, land use datais-used-in-pa~cutar
for land evaluation or land management. Unknown or worst land use data
prevent further sustainable land uses or inhibit government policy in managing
their land.
Analysis of remote sensing imageries was currently dominated by tone
analysis as it is supported by wide range of image analysis softwares. However,
latest trend on remote sensor technology, which produces higher spatial
retsolution, reveals many aspect of interpretation, not only tonal data.

New

technology contained in SPOT, Landsat ETM+, IKONOS or Quickbid allow users

extract many kind of data. One of those important characteristics is texture.
Unfortunately, texture has not been fully supported yet. Description of
texture definition is not yet completed and studied. This research indebted to

contribute on texture analysis based on simple statistical characteristics. The
results are promising, but intensive study is still required and extensive test
should be implemented on wide range of sensor or data.

AUTHORS DECLARATION

I hereby declare that 1 am the

sole author of the thesis titled "LAND USE

DISCRIMINATION BASED ON TEXTURAL CHARACTERISTICS"

Bambang Hendro Tn'sasongko

LAND USE DISCRIMINATION
BASED ON TEXTURAL CHARACTERISTICS

Bambang Hendro Trisasongko
9983108

Thesis as a partial fulfillment leading to the degree of
Master of Science in Soil Sciences

GRADUATE PROGRAM
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2002

APPROVAL

Title
Authsp

: Land Use Discrimination based on Textural Characteristics
: Barnbang Henelre Trisasengks
9983108 1 TNH

Study Program: Soil Sciences

Approved

Board of Supervisors :

Dr. Boedi Tiahiono

Dr. M. Nur Aidi

Chair

Member

Study Program of Soil Science

Head,

/c- L

~

Prof. Dr. Ir. Sudarsono, MSc

Date: 31 July 2002

L
nuwata, MSc

BIOGRAPHY

Bambang Hsndro Trisassngko was born on 3 September 1970 in Batu, Malang.
He completed Elementary School and Junior High School in Turen, Malang.
Senior High School diploma was obtained in Malang. In 1988, he continued his
undergraduate study at Bogor Agricultural University and took study program of
Soil Sciences with minor in Remote Sensing under guidance of Ir. Mahmud Arifin
Raimadoya, MSc and Ir. Tatat Sutarman Abdullah. Undergraduate thesis with
focus on development inpuvoutput program for image analysis was defended in
September 1993.
In 2000, he pursued graduate program on Soil ScienceILand Evaluation in Bogor
Agricultural University. For the thesis, he studied textural analysis applied on
aerial photograph, which may be replicated into another panchromatic systems
such as radar or panchromatic channel of multispectral images. In graduate

program, he was supervised by Dr. Boedi Tjahjono and Dr. M. Nur Aidi.
He and his wife, Dyah Retno Panuju were gifted a daughter, Annisa Palupi in
1998.

ACKNOWLEDGEMENTS

Finally, I did.
First and most, I thank Allah SWT for allowing me alive and get many
experiences in this world. Plenty "rahmats"have been given makes me easier to
complete the thesis.
I owe a great deal to Dr. Boedi Tjahjono who sewed as chair of the board

of supervisor and teach anything I do not know in the field. Terima kasih Pak

Boedi.
Dr. Nur Aidi as member of the committee made significant contribution to
many aspects in the thesis, particularly in statistics. Thank you for patience and
constant question: "manatesisnya dik Barnbang ?'. Terirna kccsih Pak Nur.
Author acknowledges and appreciates financial support provided by Dr. US
Wiradisastra. He also help in many ways supporting my study in Soil Science.

Terima kasih PC& Uup.
Sewrral persons have a g m t deal suppart both financial and moral. I
thank my former adviser (actually is still), Pak Mahrnud Raimadoya for being a
father and friend in the same time. He is responsible for introducing me to the
fascinating world of radar and image analysis. Terima kasih Pak Mahmud.
Staffs of Remote Sensing Lab. (Dr. Komama Gandasasmita, Dr.
Ardiansywh, Dr. Baba Barus and Khursatul Munibah, MSc) create an exceUent
academic atmosphere. Big thanks due to my colleagues in lab Diar "Dicky"
Shiddiq and Manijo. Warm help from friends in LAPAN, particularly Bu Ita, Pak
Santo and Pak Sudan is acknowledged.
Uncountable supports haves been provided by my family both in Turen,
Malang and Ngadiluwih, Kediri. Thank you Pipit, for any help both in lab or
home. I acknowledge help from Dr. Kardiyo Praptokardiyo's family since Istayed
in Bogor. Thank you all, may God bless on you.
This work is dedicated to Ibu' and Bapak, Ibu' and Bap& in Kediri, Dyah
and Palupi, who cheer my life at the beginning and afterwards.

TABLE OF CONTENTS
Preface

Acknowledgements

...............................................................................

Chapter I.Introduction
Aerial Photograph ..............................................................................

........................................................

Digital Image. Tone and Texture

Objectives ...........................................................................................

.................................

.

Chapter 2 Common Practices in Image Analysis
Overview on Textural Transformation ..............................................

Grey-Tone Spatial Dependence Matrix (GTSDM) ............................
Remarks on Co-Occurrence Matrix Techniques ................................

..........................
Assessing the Accuracy .....................................................................

Classification Techniques: Unsupervised Approach

Chapbr 3.Simple Statistical Texture Classification (SSTC)

................

Descriptive Measures of Distributions for Texture Measurement ......
ClassificationlSegmentatian Using Non-parametric Classification ....

Accuracy Assessment ........................................................................

......................................................

.

Chapter 4 Rules of the Investigation
Sites ....................................................................................................
Methods ..............................................................................................

..............................................................................

Chapter 5. Discussion
Sampling Technique. Preprocessing and Basic Statistics

.................

Texture Characterization by Using Basic Statistical Elements ..........
Class Separability Performance between Operators .........................
Texture Transformation and Classification .........................................
Accuracy Assessment based on Field Observation

.

Chapter 6 Concluding Remarks

...........................

..............................................................

Conclusions ........................................................................................

.......................................................

Suggestions .............................

References

llST OF FIGURES

Figure 1. Central pixel and its neighborhood................................................
Figure 2. Orientation on Haralick's method ..................................................
Figure 3. Sequential clustering processes .................................................
Figure 4 . Iterative processing in ISODATA clustering .................................
Figure 5 . Mathematical representation of error matrix .................................
Figure 6. .Convolutionprocess.................................p-.................-.---.-....
...-.-A
Figure 7. Box classifier decision boundaries in 2-d feature spaces .............
Figure 8. Modified box classification (MBC) decision boundaries................
Figure 9 . Research sites ...............................................................................
Figure 10. Research framework ...................................................................
Figure 11. Sub-images on the sites .............................................................
Figure 12. MEA performance on hypothetical image ...................................
Figure 13. Texture Transformation on Site I.................................................
Figure 14. Texture Transformation on Site 2 ..............................................
Figure 15. Texture Classification on Site 1 ..................................................
Figure 16. Texture Classification on Site 2 .................................................
Figure 17. Reference image derived from GPS mapping ..........................

LIST OF TABLES

Table 1. Statistical properties of data ........................................................... 25
Table 2 . Class discrimination by using minimum-maximum values in MBC

27

Table 3. Separability analysis on tea vs . forest in site I................................ 28
Table 4 . Separability analysis in site 2 .........................................................

29

Table 5. Thresholding in site I...................................................................... 33
Table 6. Thresholding in site 2 ...................................................................... 34
Table 7. Omission-commission matrices in site 1 .........................................

38

Table 8. Omission-commission matrices in site 2 ......................................... 39
Table 9. Overall accuracy of site 1................................................................

40

Table 10. Overall accuracy of site 2 ............................................................ 41

Chapter i
INTRODUCTION

Remote Sensing is defined as the technique of obtaining information about
objects through the analysis of data collected by special instruments that are not
in physical contact with the objects of investi~atjan(Av@ryand Berlin, 1992). As
such, remote sensing can be -regard&- as --ffo#r

a distance,

Remate sensing thus dift'en from in situ sensing, where the instruments are
immersed in, or physically touches the objects of measurement, such as portable

soil pH-meter.
In this decade, many remote sensing sensors have been developed for
civilian applications, particularly for earth monitoring. In the early years of remote
sensing development, peopllEtlscientistswere using aerial photogmph to mr?duct
monitoring. In the further development, electromagnetic devictss were used
instead of using photographic sensors,
In Indonesia, both systems are widely implemented. For instance, forestry

sector still uses aerial photograph, while also uses landsat TM or SPOT data.
The data are used in different applications. Aerial photograph is utilized to collect

base informations such as topography, timber mluncz pterdictjen, etc

In the

other hand, Landsat TM or SPOT are used for annual monitoring.

Aerial Photograph
Aerial photograph analysis involves the identication and delineation of
specific features recognized by their distinct signatures (combinations of image
characteristics). The characteristics are tone, texture, pattern, shape, size,
shadow, site, situation and association. Additionally, examination of the aerial
photograph stereoscopically enables the interpreter to observe the vertical as
well as the horizontal spatial relationships of the subject features. Due to the
complexity of the interpretative process and the wealth of data within the aerial
photo, accurate photo interpretation requires cansiderable expertise. The

accuracy of the interpretation fully depends on the quality of photography and the
analyst experience.
Photographic systems acquire spectral information with films of various
spectral sensitivities. In order to maximize photo-interpretation result, it is
important to select a film type, which will provide maximum contrast between
dierent plant communities. Choices available for camera systems are color,
color infrared (CIR) and panchromatic (black and white) films. First and widely
implemented in Indonesia is panchromaticfilm.
Most aerial photograph analyst in Indonesia is still using manual
interpretationtechnique. The technique has disadvantage since human ability is
a major factor in analysis. This disadvantage may leads to inconsistency and
inaccurate result, depends on experience of the analyst.
Recent development in computer and computing creates breakthrough in
utilization of aerial photographs. In this decade, digital photogrammetry was
widely introduced and followed by the industries creating tools for analysis.
However, these tools mostly developed for specific utilizations such as
topographic dsrlvation, utility mapping such as parcels, telecommunications,
electricities, etc. Development of digital photogrammetry or interpretation for
natural resources were less. The use of single or limited bandlchannel and no
standard procedure in aerial photograph processing inhibits further development
in natural resources applications. This research focuses on utilkatiin of digitized
aerial photographfor land use discrimination.

Digital Image, Tone and Texture
Digital image as a pictorial information is represented as a function of two
variables ( x , ~ ) .The image is stored in storage media as a two-dimensional array.
If LX = {1,2, ...,Nx) and Ly = {1,2, ... , Ny] are the X and Y spatial domains, then Lx x
Ly is the set of resolution cells and the digital image I is a function which assigns

some gray-tone value G

E

fl,2, ... ,Ng] to each and every resolution cell; I: Lx x Ly

ir G. Various two-dimensional analyses are performed on Ito achieve specific

image processing task such as restoration, enhancement and classification.
Classification of pictorial data can be done on a resolution cell basis (such
as in identifying crop category of a resolution wll on satellite imagery) or on a
block of contiguous resolution cells (such as in identifying crop category of an

entire agricultural field extending over a large number of resolution cells). The
most difficult step in classification pictorial information from large block of
resolution cells is that of defining a set of meaningful features to describe the
pictorial information from the block (Haralick, st a/, 1973).
In search for meaningful features for describing pictorial information,
Haralick ef al (1973) described three fundamental pattern elements used in
human interpretation: spectral, textural and contextual.

Spectral features

describe the average tonal (grayscale) variation in various channsllband.
Texture features contain information about distribution of grayscale variations
within specific channel. Contextual features contain information derived from
blocks of pictorial data surrounding the area being analyzed. Context, tone and
texture are always present in the image, although at times one feature may
dominate others.

In a small observation area, tone and texture dominate

interpretation process,
The notion of texture admits to no rigid description. Texture may be
defined as "something composed of closely interwoven elements".

The

descrimon of interwoven elements is intimately tied to the idea of texture
resolution, which one may think of as the average amount pixels for each
discernible texture element. If this number is large, one can attempt to describe
the individual elements in some detail. However, as this number near unity it
becomes increasingly difficult to characterize these elements individually and
they merge into less distinct spatial patterns (Ballard and Brown, 1982).
Jensen (1996) provided another description of texture as follow. A discrete
tonal feature is a connected set of pixels that all have the same or almost the
same gray shade (brightness value). When a small area of the image (e.g., a 3 x
3 area) has little variation of discrete tonal features, the dominant property of that
area is a gray shade (tone). Conversely, when a small area has a wide variation
of discrete tonal features, the dominant property of that area is texture.
Land use discriminations could be, done by using different approaches. In
aerial photograph, we use some standard framework to differentiate land uses
such as Nine Interpretative Keys (Sembilen Unsur lnferprefeisr). The framework
uses nine different characteristics i.e. tone, texture, pattern, shape, size, shadow,
site, situation and association. Discriminationof land use dasses empbys one or
more distinct keys in order to gain more acceptable results. Unfortunately, the

framework was designed for manual interpretation. Since digital processing has
different approach in interpretation scheme, adapting framework is necessary.
However, some characteristics such as texture are challenging since it is exist in
all natural imageries.
It is often found that classes of land coverlland use may be discriminated in
digital imagery on the basis not only of their characteristic tone (i.e. mean digital
number value) but also on their texture.

Several authors have attempted

qualitatively to define texture. Jensen (1996) notes that texture dominates when
a small area has a wide variatin of discrete tonal features.
Several investigations on high-end image analysis softwares such as Erdas
Imagine 8.3.1 and PC1 EasiPace 6.3 showed that texture dassifkation
procedures are not or less supported. These sofhrvares basicalfy provide texture
transformations only. Therefore the results of these processes are in form of
texture imagers instead of texture-classified images. This condition is key factor

in the research. Some proposed technique will be described in foilowing
chapters.

0 bjectives
The research has three primary goals:
Assess textural characteristics that derived from panchromatic images i.e.
aerial photograph
e

Analyze textural transformation methodologies, particularly for land use
discrimination.
Develop framework for land use discrimination based on texture
classification.

Chapter ll
COMMON PRACTICES IN lMAGE ANALYSIS

Ovewiew on Textural Transformation
There is no rigid classification of textural analyses. Some authors used
different classifications to describe developed techniques. Haralick et a/. (1973)
stated that early image texture studies have employed (i) autocorrelation
functions, (ii) power spectra, (iii) restrided first-and second-order Markov meshes
and (iv) relative frequencies of various gray levels on the unnomalized image.
Meanwhile, Pratt (1991) described seven methods for analyzing texture features:
(i) fourier spectra methods, (ii) edge detection methods, (iii) autocorreiation
methods, (iv) decorrelation methods, (v) dependency matrix methods, (vi)
microstructure methods and (vii) singular value decomposition methods. Detailed
explanation is found on Haralick eta/. (1973).
In recent years, authors dramatically generalized texture analysis methods
into only several categories. Chen (1995) stated that texture extraction technique
falls roughly into five categories: structural, statistical, spectral, stochastic and
morphology. Structural approaches are based on the theory of formal languages:
a texture image is regarded as generated from a set of texture primitives using a
set of placement rules. These approaches work well on deterministic textures but
most natural textures, unfortunately, are not of this type. A two-dimensional
power spectrum of a texture image often reveals the periodicity and directionaiity
of the texture. For example, a coarse texture tends to generate low frequency
components in its spectrum while a fine texture will have high frequency
components. Stochastic madel such as Markov random field can also be used
for texture extraction. This approach considers texture as realizations of a
random process. In mathematical morphology approach, a texture image is
filtered by a sequence of morphological operations with stnrduting elements of
various sizes. Probably, the most utilized technique is statistical approach. From
a statistical point of view, textured images are complicated pictorial patterns on
which sets of statistics can be obtained to characterize these patterns (Chen,
1995). This research limits its perspective only on statistical approach.

Let i
= {I,

=

{I, 2, ..., Ni) represents configuration on horizontal s m a l domain, j

2, ... , Nj) represents configuration on vertical spatial domain and k = {I, 2, ... ,

Nk) on spectral graylevel, then data arrangement could be described based on
spatial distance between central data (pixel) and its neighborhood. The
arrangement then creates texture.

Figure 1. Central pixel and its neighborhood

We have seen that pixel e is acting as a central pixel. If spatial distance is
assumed 1 (3 by 3 neighborhood), the neighboring pixel is constructed by pixels
a, b, c, d, f, g, h and i. According to Ballard and Brown (1982), the 3 by 3
arrangement above is usually called Texel (Texture? Element). In order to
describe smoothness of texture quantitatively, discrimination between
arrangements (texels) was needed. Most statistical techniques develop their
approach on texel discrimination.
Many researchers have developed texture analysis algorithms. However,
there are no such standard methodologies for selection best approach. However,
there is textural algorithm worth noted based only its wide implementation. The
algoriihm is Grey-Tone Spatial-Dependence Matrix (Haralick, et a/., 1973).

Grey-Tone Spatial Dependence Matrix (GTSDM)
Some techniques have been developed based on statistical approach.
One of the first techniques and probably the most popular used was developed
by Haralick et a\. (1973). The technique is called Grey-Tone Spatial-Dependence

Matrix (GTSDM). GTSDM constructs matrices by counting the number of

occurrences of pixel pairs of given greylevels at given displacement. Statistics
like contrast, energy, entropy and so forth are then applied to the matrices to
obtain texture features (Chen, 1995).
In Haralicks method, arrangement in texetls not only depends on spatial
distance, but also on neighboring pixel position. Arrangement between pixels is
determined by angle between central and neighboring pixel. Definition of rotation
angle in Haralick's is presented in following figure.

Figure 2. Orientation on Haralick's method

In this neighborhood, similarity between central pixel and its surrounding
pixels is then computed in specitic angle. Similarities in whole image construct a
I stated GTSDM is
matrix usually called Dependence Matrix. Haralick et a!. (973)
a two-dimensional array that provides the conditional joint probabilities of all

paitwise

combination of

pixels within

&fined

computation window.

Mathematically, pixel pairs relationship could be defined as:

where d represents interpixel sampling (6patial) distance and a represents
orientation or rotation angle. Construction of dependence (co-occurrence) matrix
LCv]is using definition as follows:

where P is the frequency of occurrence of graylevels iand j, and n is the total
number of pixel pairs based on observation window and defined interpixel
sampling distance (d)

Remarks on Co-Occurrence Matrix Techniques
Radiometric information plays an important role in remote sensing
imageries. The radiometric information is represented by digital number or
brightness value in a digital imagery. Recently, almost all algorithms applied on
remote sensing image analysis are considering radiometric data collected by
sensor as main data. Thematic extraction techniques such as classification
employ digital number in their algorithms. Most multispectral imageries use 8-bit
brightness scale, white other remote sensing platforms such as radar use 16 or
32 bit.
Since brightness value is relatively deep, computation time and size of
occurrence matrix are important. For instance, if user use Landsat TM image
with 8-bit depth or 256 greyscales, the algorithm will produce matrix as big as 256
by 256.

Application on highly detailed imagery such as Radarsat, which

produces 16- or 32-bit data, implies on extremely high volume of occurrence
matrix. Sixteen-bit data will produce 65,536 x 65,536 matrix. Thirty-two bit data
will have square matrix of 4,294,967,296. The matrix size is one major obstacle
in texture image analysis.
Schowengerdt (1997) stated that to reduce the computation burden, it is
common practice to reduce the DN quantization (by averaging over adjacent
DNs) to some smaller value and to average the co-occurrence matrix for different
spatial directions. This technique might be applied on some images, particularly
used by common image processing applications such as industrial imaging. On
these kinds of imageries, specifically defined individual tone is not practically
considered as an object in a scene. Therefore, techniques that alter pixels
usually used to enhance information extraction. On remote sensing imagery,
these kind of processing should be carefully taken. Alteration of digital number
may shifts information gathering or creates bias on image understanding. In
remote sensing image analysis, changing DN value of pixels is one of major
concerns. Image enhancement such as stretching may create misinterpretation
in digital image classification. In the simpb word, greyscale manipulation (i.e.
greyscale compression) such as technique described by Schowengerdt is rather
impractical on remote sensing data.
These methods are widely implemented on some softwares. However,
implementation is still based on their origin i.e texture transformation. There are

no further researches to develop texture classification scheme based on the
methods.

Classification Techniques: Unsupewised Approach
Unsupervised classification is also called clustering, because it is based on
the natural groupings of pixels in image data when they are plotted in spectral
space. Cluster uses all or many of the pixels for its analysis and has no regard
for contiguity of the pixels that define each cluster.

There are several

methodologies to perform clustering.
Most basic technique in clustering remote sensing image is Sequential
Clustering. In sequential method, pixels are examined one at a time. The
spectral distances between each analyzed pixel and the means of previously
defined clusters are calculated. Each pixel either contributes to an existing
cluster, or begins a new cluster, based on the spectral distances. Clusters are
merged if too many are formed.
The sequential method requires little setup or preparation. There is only a
few parameters need to be specified. The method usually performs faster than
another approach such as ISODATA method. However, Sequential Clustering is
slightly biased to the top of the image data. In the other word, this method is
geographically biased and depends on location of first data being processed.
ERDAS (1991) also reported that this method is parametric, meaning that the
algorithm works on the assumption that the data distribution is normal.
ISODATA stands for "Iterative Self-organizing Data Analysisn. ISODATA
method is somewhat similar to the sequential method. Both data use minimum
spectral distance to assign a cluster for each candidate pixel. The major
differences between both methods am: (i) the ISODATA process begins with a
specified number of arbitrary cluster means; and (ii) ISODATA processes
repetitively so that those arbitrary means will shift to the means of the clusters in
the data (ERDAS, 1991).

Figure 3. Sequential Clustering Processes (Jensen, 1996)

0

10

30

40

Bvml4
Wlnras Value

Since ISODATA clustering is iterative, the method is not geographically
biased to first location being processed.

ERDAS (1991) also noted that
ISODATA is not as parametric as the other clustering methods and produce good
results for data that are not normally distributed. The main disadvantage of
ISODATA is slow performance due to its multiiiterative processes.
Figure 4. Iterative processing in ISODATA clustering (ERDAS, 4991)
!SOU&" A, ',?st
!S(;,>AT.4
:
'

f

tr

!.............

....... , a

+

.,

8 2

"n

CIIMV'

CU:UIMI

,c--r~,.-

'

%&,-,.,-

* :

;
L
Band A
d w i fite VPILIBS

-:.-

"
,
-2.:
rlew meens and etvd;,'?

milan%In two-i:lmerlslorai Toc>clrlml 5r:aar

a l ~ t : l f r ~,-!l:si+(
a~

.I,,

Ainioary i3iui:ter'j

:';is$

f i ~ l i dA
d ~ r ill ~vatoes
!

Assessing the Accuracy
Accuracy assessment determines the quality of the information derived
from remote sensing imagery. Accuracy assessment can be qualitative or
quantitative.

The purpose of quantitative accuracy assessment is the

identification and measurement of errors in the classified image. Quantitative
assessment involves comparison between classified image and reference data.
The reference data itself is assumed to be correct.
Congalton and Green (1999) stated that all accuracy assessment include
four fundamental steps: (i)designing the sample; (ii) collecting data for each
sample; (iii) building and testing the error matrix; and (iv) analyzing the resutts.
Each step must be rigorously planned and implemented. Effective accuracy
assessment requires (I) design and implementation of unbiased sampling
procedures, (2) consistent and accurate collection of sample data, and (3)
rigorous comparative analysis of the sample data.
An error matrix compares information from reference sites to information
derived from remote sensing data. The matrix is a square array of numbers set
out in rows and columns that represent the labels of land use class assigned to
particular category in one classification relative to the label of land use class
assigned to particular category in another dassification.

One of the

classifications, usually the columns, is assumed to be correct and is called
reference data. The another classification situated in rows is used to display
classified labels derived from remote sensing data.
The error matrix can be considered effectiie way to represent accuracy due
to its capability to describe both error of inclusion (commission emr) or error of
exclusion (omission error). A commission error is defined as including an area
into a category when it does not belong to that category. An omission error is
excluding that area from the category in which it truly does belong. Every error is
an omission from the correct category a commission to a wrong category
(Congalton and Green, 1999). In addition, the error matrix can compute another
accuracy measures i.e. overall accuracy, producer's accuracy and user's
accuracy. Mathematical representation of error matrix is presented in following
figure.
Let assume that n pixels are distributed into k2 array where each sample
is assigned to one of k categories in the classified data (rows) and independently

to one of the same k categories in the reference dataset (columns). Let n,
denote the number of pixels classified into category i where i = 1, 2, ... , k in
remote sensing image and category j where j

=

1, 2, ..., k which represents

reference data.

Figure 5. Mathematical representation of error matrix
(Congalton and Green, 1999).

j

= columns = refemnce
I

i=
=
classification

column total

-

2

...

row total

k

ni+

1

n1+

2

n2+

...

...

k

nk+

n+j

n+1

n+2

...

n+k

n

Number of pixel that classif'ied into category i in the remotely sensed
classification is formulated as (Congalton and Green, 1999):

and number of pixels classified into category j in reference data is denoted as:

Overall accuracy between remotely sensed classification and the reference
data can then be computed as follows (Congalton and Green, 1999; Jenmn,
1996) :

Chapter Ill
SIMPLE STATISTICAL TEXTURE CLASSIFICATION (SSTC)

There are two steps used to classify image based on textural information.
First step is employing texture measurement, which transform original image into
textured image. The second is classification or segmentation procedure that
provides texture-classified image.
Schowengerdt (1997) proposes method to reduce computation on cooccurrence matrix that is averaging over adjacent digital number. On non-remote
sensing data, the technique may be considered. On the other hand, rernotesensing imagery contains specific data in the pixel. Therefore retaining data is
one of major concern in digital image analysis. Objection on Schowengerdt's
statement is main direction in this research. This objection showed importance to
pursue a comprehensive experiment starting with basic statistics. A simple basic
statistics framework will be designed by employing descriptive measures of
distributions.
Local statistic methods could be used as starting point to assess existence
of textural information, Computation of statistical measurement is practically
same with technique usually used for assess basic statistic of an image. The
only difference is applied data. Basic statistic computation is usually applied on
whole digital number with smallest unit of observation is a pixel. Later, we! will
discuss on texel as smallest unit of observation.

Descriptive Measures of Distributions for Texture Measurement
There are two kind of measures usually used to describe distribution
function of data: (i) measures of centrality; and (ii) measures of variability
(Barnes, 1994). Measures of centrality refer to the location or centrality of a
probability distribution function and consist of three theoretical quantities i.e.
mean, median and mode. The mean is mathematically defined as:

p=

xp(x)

p = J xf (x)&

for discrete distributions
for continuous distributions

Mean is the most commonly used average of a distribution and can be
obtained readily with data processing. However, another measure could be used
also for measuring central tendency. Median is defined as the middle value when
data are arranged in an array according to size (Longley-Cook, 1970).
Another useful measure of the center tendency is the mode. When there
are a number of values at each point or in each class, the point or class with the

greatest number is called the mode. When a frequency curve has been drawn,
the mode is the maximum point on the graph. Occasionally there is no mode or
more than one mode (Longley-Cook, 1970). This condition creates less useful
implementation on this research since each texture needs to be characterized by
its histogram.
Two separate sets of data may contain the same number of items and have
the same mean but one set may be much more dispersed or spread about the
average value than the other. A measure of dispersion or variation from the
mean is needed to help define the distribution more fully.
Longley-Cook (1970) describes six types of measurement to assess
dispersion.
I.The Range. The difference between the largest and the smallest values.

2. The 10-90 Percentile Range. The difference between the 1 0 ~and 9oth
percentile points.
3. The Semi-lnterquartile Range or Quartile Deviation. One-half the difference

between the first and third quartiles.
4. The Avenge Deviation from the Mean or Mean Devi9tion. This is the

arithmetic mean (average) of the individual absolute values of the deviations
from the mean.
5. The Standard Deviation or its square, the Varience. This is mod generally

used measure of dispersion.
6. The Half-Width. Onehaif of the width of the frequency cuwe at a height on

the y axis equal to one-half of the height of the modal point.

In this research, author used four statistical descriptors for characterizing
land use texture i.e. Greyscale Range (RGE), Mean (MEA), Variance (VAR), and
Entropy (ENT). Fitst three descriptors were adopted from Longley-Cook (1970).
The last method was proposed as additional and comparative descriptors.
A distribution can be categorized by a single, information, measure called

Entropy. This measure is the average uncertainty of the various proportions, and
is calculated by the formula (Johnston and Semple, No Date):

where
is the proportion in the i-th component of the distribution
pi
n
is the number of components in the distribution, and
is the information measure (Entropy)
H
Whole descriptors were calculated on local area and adopting convolutibn
kernel strategy. Convolution formalizes the notion of moving a mask around the
image and recording the dot product of the mask with each page neighborhood.
Following figure will visually explain the mask and moving scheme.

Figure 6. Convolution process

ClassificationlSegmentation Using Non-parametric Ciassification
Terms of Classification and Segmentation in this thesis are used
interchangeably, since the outputs are same. Classification terminology usually
found in remote sensing image analysis texts. On the other hand, most computer

vision books refers it as Segmentation.

However, Classification and

Segmentation have not perfectly same meaning.
Shapiro and Stockman (2001) stated that the term of image segmentation
refers to the patiition of an image into a set of regions that cover it. The goal in
many tasks is for the regions to represent meaningful areas of the image, such as
crops, urban areas and forests of a remote sensing image.
Classification strategy is closely related to the amount of data, including
its variable (in remote sensing image analysis means number of bands).
Recently, many classification algorithms have been developed based on
multivariate statistics or combined with another approach such as fuzzy or neural
network. However, classification methodology for single band remote sensing
data is less constructed.
Classification algorithms may be grouped into one of two types:
parametric or non-parametric. Parametric algorithms assume a particular class
statktical distribution, commonly the normal distribution, and require estimates of
the distribution parameters, such as the mean vector and covariance matrix, for
classification.

Non-parametric algorithms make no assumptions about the

probability distribution and are often considered robust because they may work
well for a wide variety of class distfibutions, as long as the dass signatures are
reasonably distinct (Schowengerdt, 1997).
Classification scheme in this research is built based on classification
strategy developed in radar image processing. This is due to similariiy condition
between single polarization radar and digital airphoto, i.e. single band data.
Several authors have developed some single-band classification approaches.
First and widely utilized approach is by implementing threshdding technique.
Second approach is based on spatial similarity and usually called region growing.
Third approach is employing edge detection mechanisms.
There are two main steps in classification by thresholding. First step is to
assign a class label to each pixel according to its intensity value:
class(x, y ) =

IN
OUT

f

if

I ( x , y ) 6 Emin, max]
I ( x , y ) e [min,max]

Then, pixel-labeling algoriihm is used to assign the same label to the connected
pixels belonging to the same class. The pixels, which have the same label, are
connected and form part of a single segment (Beaulieu and Najeh, 1997).
Region growing algoriihm requires two parameters to initiate
segmentation i.e. a window size and a threshold. A window is associated to each
seeded pixel where initial values are estimated. It is assumed that an optimum
size window exists that differentiates the most the values related to each seeded
pixel windows. To decide if a pixel is aggregated to a region, a difference
between tested pixel window and the initial one is obtained; if this value does not
exceed a certain threshold, the tested pixel is incorporated into the region (Lira et
a/., 1996). Authors including Shimabukro et al. (1997) and Costa et al. (1997)

have reported some successful works. However authors notice about difficulties
and subjectivity on initializing seed region.
Third approach basically uses edge detection algorithms such as Sobel,
Laplacian or higher technique such as Mough transform. This approach was less
implemented since the algorithm only provides boundary for different class and
does not provide "label" for each class.
This research only focus on thresholding technique due to its
computationally effiient and reliable for basic discrimination between groups of
data. Based on this technique, author proposes Modifled Box Classifier (MBC).
Modified box classifier is developed based on Box Classifier with only one feature
space. Schowengerdt (1997) state that Box classifier is perhaps the simplest of
all classification methods. A set of k-dimensional boxes, centered at estimated
classes mean vectors, are placed in k-dimensionalfeature space. If an unlabeled
pixel vector lies within one of the boxes, it is assigned that class label. Decision
boundaries are set by vectors of means and standard deviations.
Modification should be done to allow digital aerial photograph to be
processed. Simple modification is applied here by using only one dimension
feature space. One dimension feature space of Figure 7 is presented in Figure 8.
In order to reduce unclassified pixels or class conflict between two or
more classes, author uses minimum greyscaledistance to mean method. This
method is selected since the algorithm is computationally simple. Although it is
designed to apply on distribution-free image, this method is also implementable
to normal-distributed image under some circumstances.

Figure 7. Box classifier decision boundaries in 2-6feature spaces
DN2

.......................................,j

i

j

Class A

i
.................................................j

.....................................

i. i.
:
:

:

j

1

Class B

,

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

b
DNI
C

Figure 8. Modified box classification (MBC) decision boundaries.
......................................

i

. .................................................
.
?
i

; {
; i

i

j

f i
Class B
j
.i .i ..............................................!

Class A

+

DNl

...................................

.
)

I

Accuracy Assessment
In this research, proposed accuracy assessment is following error matrix
technique as described by Congalton and Green (1999). l-fowever, modification
will be applied in order to fit the cumnt condition. In their book, Congalton and
Green (1999) used sampling technique to collect brightness value data. This
sampling data will be used to compute error matrix. Instead of using sampling
method, this research uses whole brightness value in order to gain error matrix.
This modification is applied to reduce user subjediity in sampling selection. The
modification has disadvantage since it requires massive computation, which
needs powerful computer systems, particularly in big image dataset.

Chapter lV
RULES OF THE INVESTIGATION

In this research, some tools are utilized i.s. computer with remote sensing
software and Microsoft Visual Basic 5 Control Creation Edition compiler. Another
device is HP ScanJet 6100 Scanner System. In fieldwork adivrty, author utilize
one unit of Global Positioning System Gamin 12XL (including downloader unit)
and a portable computer (notebook).

Sites
In this research, author used two locations in Cianjur Regency for analysis
based on aerial photograph data. First location was in Pacet and relied on southeast slope of Mt. Gede-Pangrango. Second location was east slope of same
mountains in area of Punwk. First image in Pacet consists of two land use
classes i.e. forest and tea. Second image in Puncak has three land use dames:
(i)forest; (ii) mixed garden and (iii) bare phase of paddy field, Figure 9 shows the

original images.

Methods
The research framework could be divided into two activities i.e. desMab
study and fieldwork. Desk study in laboratory is intended to gain comprehensive
knowledge about research mainstream in texture analysis. In this study modeling
on texture in different images and applications have been studied. In some
study, the modeling is not enhanced by intensive fieldwork. In this thesis, actual
information based on fieldwork is highly considered. Information collected will be
used as reference map. Complete framework is presented in Figure 10.

Figure 9. Research Sites
Site 1

(Pacet)

Forest

Tea

Site 2
(Puncak)

Forest

PaddvIBare Land

Mixed Garden

Good-conditioned and geocorrected aerial photographs are selected and
scanned in 150 dpi. The use of higher resolution may be applied. However,
higher resolution significantly increases data and in particular resolution, no
information gained further. Selection of land usg class is then performed. First
test of Simple Statistical Texture Classification (SSTC) will be applied on
imagette with simple class discrimination. Another test is the apptied on more
complex imagettes.
In order to gain knowledge on behavior of SSTC, this research
implemented different parameter on distance lag. The distance lag is used to set

convolution window.

Relationship between distance lag

(4 and

convolution

window (w) is defined as

In this research, author used 4 types of convolution windows that are 3x3, 5x5,
7x7, and 9x9. The use of 3x3 window usually produces crisp output but less

generalized polygon.
Fieldwork is intended to map selected areas. Usually, field check is done in
only one or mote locations related to its class. This approach has limitation since
actual boundary is not particularly defined. Usually samples are taken far from
the boundary. In order to minimize bias on boundary areas, author use field
mapping method. However, since mapping is done in the field, this method
needs intensive work on the field. Fieldwork will utilize Garmin GPS to derive
land use boundaries. Since GPS system limits obsentation points to 500 nodes,

a portable computer (laptop or notebook) and GPS downloader unit are required.
This step produced a reference map.
Both imagsprocessing approaches are then assessed based on reference
map. Quality and performance of each method is assessed by using error matrix
as described in previous chapter.

Gaoometitad

/

/

Photograph

t

1

1

Scanning on 150 dpi

L-T-

L.,

1

wlectim

I X n d

7
I
-

3
-

L/

1

TextureAnalj%is
with diffarant lag

--

7

4
-

Field work and

I

A

Texture Characterization

and Classification

1

1

Reference Map

Field-baasd
Accuracy Assemment

1

i i

L-v\

1

I

I__-

Chapter V
DISCUSSIONS

Sampling Technique, Preprocessing and Basic Statistics
The SSTC requires representative sampling area for discrimination

purpose. Basic appropriateness of sampling area has been selected based on
visual recognition of land use classes and field obsetvation. Sampling area is
selected in the middle of the land use coverage in order to reduce uncertainty,

which particularly occurred in smooth or undetermined border. Each sampling
area consists of 30 by 30 pixels. For Site 1 (Pacet), two sampling areas have
been selected consists of forest and tea land use classes. For Site 2 (Puncak),
three areas have been selected according to available land use classes i.e.
forest, paddy and mixed garden. Names of land use class have been determined
in field observation. Sub-images for both sites are presented below.

Figure 11. Sub-images on the sites (images enlarged 3 times).

Forest

Tea

I

1

1 Forest

Paddy

Mixed Garden

In above images, it is clear that texture is a noteworthy property particularly
in panchromatic image such as aerial photograph. However, aerial photograph
has difficulties in maintaining tone since it was developed by non-electromagnetic

sensor and developed by chemical reactions. Photograph appearance is strongly
determined by time of photographic processing. Aerial photograph also has poor
radiometric calibration and validation, which usually used to maintain image
quality products. A fact that forests in Site 1 and Site 2 are visually dierent
particularly for tonal arrangement is basically determined by unbalanced quality
product. By using satellite images, the weaknesses may be avoided.
This research is intended to characterize texture in different condition. In
this research, conditional aspect is only determined by spatial distance (lag)
between central pixel and its neighborhoodin a convolution kernel. The research
uses 4 types of lag, i.e, 1 (or 3x3 kernel), 2 (or 5x5 kernel), 3 (or 7x7 kernel) and
4 (9x9 kernel).

The lags can be used to assess sensitivity in texture

discrimination.
Preliminary processing for basic statistical computation is required for
describing the data.

In this research, basic statistical computation involves

search for minimum and maximum value, and calculating mean and standard
deviation. Result is described in Table ?.

Texture Characterization by Using Basic Statistical Elements
In this research, texture characterization is derived from analogy of box
classification as described in Chapter Ill. Standard box dassifier uses two or
more feature space to describe each class on the image. Since this research
used panchromatic image, modification of standard box classifier is proposed. In
this time, the modification is simply called Modified Box Classifier (MBC).
The MBC requires basic statistical information based on sample images
that describe textural information of each land use. Those basic informations are
mean and standard deviation. Textural information was then derived by
characterizing each mean and standard deviation on each class. With those
statistical informations, boundary (minimum and maximum values) of each land
use class may