Classification of remotely sensed images using the gene sis fuzzy segmentation algorithm

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CLASSIFICATION OF REMOTELY SENSED IMAGES USING
THE GENESIS FUZZY SEGMENTATION ALGORITHM
By
A
PROJECT REPORT
Submitted to the Department of electronics &communication Engineering in the
FACULTY OF ENGINEERING & TECHNOLOGY
In partial fulfillment of the requirements for the award of the degree
Of
MASTER OF TECHNOLOGY
IN
ELECTRONICS &COMMUNICATION ENGINEERING
APRIL 2016

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CERTIFICATE
Certified that this project report titled “Classification of Remotely Sensed Images Using the
GeneSIS Fuzzy Segmentation Algorithm” is the bonafide work of Mr. _____________Who
carried out the research under my supervision Certified further, that to the best of my knowledge
the work reported herein does not form part of any other project report or dissertation on the basis
of which a degree or award was conferred on an earlier occasion on this or any other candidate.
Signature of the Guide Signature of the H.O.D
Name Name

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DECLARATION
I hereby declare that the project work entitled “Classification of Remotely SensedImages Using
the GeneSIS Fuzzy Segmentation Algorithm” Submitted to BHARATHIDASAN
UNIVERSITY in partial fulfillment of the requirement for the award of the Degree of MASTER
OF APPLIED ELECTRONICS is a record of original work done by me the guidance of
Prof.A.Vinayagam M.Sc., M.Phil., M.E., to the best of my knowledge, the work reported here
is not a part of any other thesis or work on the basis of which a degree or award was conferred on
an earlier occasion to me or any other candidate.
(Student Name)
(Reg.No)
Place:
Date:

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ACKNOWLEDGEMENT
I am extremely glad to present my project “Classification of Remotely SensedImages Using the
GeneSIS Fuzzy Segmentation Algorithm” which is a part of my curriculum of third semester
Master of Science in Computer science. I take this opportunity to express my sincere gratitude to
those who helped me in bringing out this project work.
I would like to express my Director,Dr. K. ANANDAN, M.A.(Eco.), M.Ed., M.Phil.,(Edn.),
PGDCA., CGT., M.A.(Psy.)of who had given me an opportunity to undertake this project.
I am highly indebted to Co-OrdinatorProf. Muniappan Department of Physics and thank from
my deep heart for her valuable comments I received through my project.
I wish to express my deep sense of gratitude to my guide
Prof. A.Vinayagam M.Sc., M.Phil., M.E., for her immense help and encouragement for
successful completion of this project.
I also express my sincere thanks to the all the staff members of Computer science for their kind
advice.
And last, but not the least, I express my deep gratitude to my parents and friends for their
encouragement and support throughout the project.

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ABSTRACT:
In this paper, we propose an integrated framework of the recently proposed Genetic
Sequential Image Segmentation (GeneSIS) algorithm. GeneSIS segments the image in an iterative
manner, whereby at each iteration, a single object is extracted via a genetic algorithm-based object
extraction method. This module evaluates the fuzzy content of candidate regions, and through an
effective fitness function design provides objects with optimal balance between fuzzy coverage,
consistency and smoothness. GeneSIS exhibits a number of interesting properties, such as reduced
over-/undersegmentation, adaptive search scale, and region-based search. To enhance the
capabilities of GeneSIS, we incorporate here several improvements of our initial proposal. On one
hand, two modifications are introduced pertaining to the object extraction algorithm. Specifically,
we consider a more flexible representation of the structural elements used for the object’s
extraction.
Further more, in view of its importance, the consistency criterion is redefined, thus
providing a better handling of the ambiguous areas of the image. On the other hand we incorporate
three tools properly devised, according to the fuzzy principles characterizing GeneSIS. First, we
develop a marker selection strategy that creates reliable markers, particularly when dealing with
ambiguous components of the image. Furthermore, using GeneSIS as the essential part, we
consider a generalized experimental setup embracing two different classification schemes for
remote sensing images: the spectral-spatial classification and the supervised segmentation
methods. Finally, exploiting the inherent property of GeneSIS to produce multiple segmentations,
we propose a segmentation fusion scheme. The effectiveness of the proposed methodology is
validated after thorough experimentation on four data sets.

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INTRODUCTION:
Inrecent years, the growing development and availability of satellite imagery with high
spectral-spatial resolution (HSSR) poses new challenges in the field of land cover classification.
An attractive method, having recently received considerable attention, is to incorporate spatial
information in order to improve the classification results obtained by traditional pixelbased
classifiers. One way to achieve this goal is to extract spatial features from fixed-size neighborhoods
around pixels and combine them with the spectral bands in a single feature vector . These methods,
however, raise the issue of scale selection, since they rely on fixed windows that are not sufficient
to identify structures of different sizes existing in the image. To address this issue, Huang et al.
proposed a framework for the automatic window selection for every pixel and the fusion of this
multiscale information. In an edge extraction technique is used to initially partition the image into
boundary and nonboundary pixels. These two sets are classified separately, and the resulting maps
are subject to various geometric operators before the final fusion step is carried out. Finally, some
other approaches incorporate spatial information into the SVM classifier, either by modifying its
decision function and constraints or by using composite kernels
A more effective alternative for integrating spatial information is to perform image
segmentation. Segmentation is the partitioning of the image into disjoint regions so that each
region is connected and homogeneous with respect to some homogeneity criteria of interest.
According to Fu and Mui most of the segmentation methods can be divided into three categories:
edge-based, clustering/feature thresholding and region-based. Edge-based methods operate on the
spatial space, searching for discontinuities in the image by examining the existence of local edges.
The extracted edges finally enclose the created objects. Watershed transformation is the most
commonly used method of this category, having been extensively employed in various remote
sensing studies Derivaux et al. propose a supervised segmentation method, where watershed is
applied to a transformed feature space of fuzzy membership values. The optimal segmentation is
finally obtained via a genetic algorithm optimization of the watershed parameters. Li et al. employ
a marker-based watershed algorithm with embedded edge information. All these approaches suffer
from a major limitation, i.e., sensitivity to local spectral variations, which typically results in
oversegmentation of the image

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On the other hand, clustering techniques operate in the spectral space, searching for
significant modes in the patterns distribution. The created clusters are then mapped back to the
spatial domain in order to form the segmentation map. Tarabalka et al. follow this strategy by
employing the ISODATA and EM clustering algorithms. The generated segmentation map is
finally combined with the SVM classification results via majority voting, to produce the final
spectral-spatial classification. A similar classification scheme is proposed in with the difference
that a weighted majority voting rule is used. An important demerit of the aforementioned methods
is the ignorance of the spatial association of pixels during the clustering process. A method coping
with this issue is presented in where the mean-shift density-estimation technique is used for data
clustering in a joint spectral-spatial space
Region-based methods rely on the assumption that adjacent pixels in the same region have
similar spectral features, and hence, most likely belong to the same class. Some methods in this
group exploit the graph representation of the image and perform segmentation by utilizing graph
theory-based algorithms. Specifically, in the minimum spanning forest (MSF) constructed in each
tree is rooted on a classification derived marker, whereas in the graph-cut algorithm is employed
for solving the metric labeling problem. However, region growing is the most commonly employed
methodology in this domain. Region growing segmentation algorithms usually start from a pixel
level and evaluate a homogeneity criterion in order to decide which neighboring pixels and/or
objects should be merged next.
The process is repeated sequentially, until a termination condition is satisfied. Fractal net
evolution approach (FNEA) is one of the most known methods of this category, which tries to
minimize the objects inner heterogeneity. The applied heterogeneity criterion utilizes both spectral
and shape information of the objects, by considering two different shape components, namely
compactness and smoothness. Hierarchical step-wise optimization (HSWO) evaluates a
dissimilarity function between all neighboring objects and the merging decision is taken via the
best merge rationale: the pair with the smallest dissimilarity is the one to be merged. Tilton
extended this method by allowing constrained merges of spatially nonadjacent regions. In all the
aforementioned methods, proper selection of the termination conditions has always been a
challenging task. The definition of a meaningful stopping criterion.
However, straightforward. So, instead of having a single segmentation, they take advantage
of the hierarchy existing in the continuous merges and create multiple ones by stopping the

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merging process at different phases. The end result is a hierarchy of segmentations, each with a
different scale, from coarser to finer ones. The demerit here is that a range of thresholds, usually
with no physical meaning, must be chosen and the resulting hierarchy must be examined by an
expert in order to obtain the most suitable segmentation.
Recently, several methods have been proposed to confront with this problem, attempting
to automatically obtain a single segmentation map from a hierarchy . Specifically, the
Classification and Hierarchical Optimization (CaHO) and the Hierarchical Segmentation with
integrated Classification (HSwC) achieve this goal by incorporating knowledge from a supervised
pixel-based classifier in the employed dissimilarity criterion. In Marker-based HSEG (M-HSEG)
markers are extracted initially from an SVM map. During the merging process, a restriction is
imposed, where regions with different marker labels cannot be merged.
Furthermore, in a binary partition tree structure is used to store the hierarchical region-merging
segmentation.
The final segmentation is obtained by tree pruning, according to a properly defined
criterion. Various segmentation methods have been suggested in the past that make use of
evolutionary algorithms, and particularly genetic algorithms (GA). GAs are universal optimization
methods, inspired from the genetic adaptation of natural evolution . Some methods use the GA to
optimize a set of parameters that control a common segmentation algorithm . In this case, each
chromosome encodes a different set of parameters, thus an entire image segmentation is completed
for each chromosome’s evaluation. The simplicity in representation is contrasted here to the high
computational load. Another category includes those methods adopting the global encoding
approach, where each chromosome encodes a segmentation of the whole image . The
representation here increases the search space complexity of the GA exponentially. Therefore,
optimal solutions can only be attained at the expense of large population sizes and after a large
number of generations.
Genetic Sequential Image Segmentation (GeneSIS) is a recently proposed algorithm for
object-based classification of remotely sensed images. It segments the image sequentially, which
is a single object is extracted at a time via a GA-based object extraction algorithm (OEA). This
allows the adoption of a simpler solution encoding, which reduces the search space complexity
considerably. In this paper, we present an integrated framework of GeneSIS that incorporates
several enhancements compared with our initial proposal. The synergetic contribution of these

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additions improves significantly both the classification accuracy and the image description
qualities of GeneSIS.
First, we introduce two constructive modifications involved in OEA with the goal to
increase the flexibility of the chromosome solutions and facilitate a better representation of the
significant ground structures existing in the terrain. Specifically, OEA evolves a population of
structuring elements placed on the image, called the basic search frames (BSFs), which are
represented by rectangular windows of varying size. BSFs are continuously relocated over the
generations, trying to find the best object for extraction. Objects are extracted as connected subsets
from the interior of BSFs. A drawback of GeneSIS is that owing to the axis-aligned encoding of
BSFs, it produces occasionally strong oversegmentation results when dealing with images
containing rotated ground truth structures. To address this problem, we adopt in this paper a more
general representation of solutions, by incorporating rotation of the BSFs. Moreover, each
candidate object of the population is evaluated in terms of three fuzzy fitness criteria: coverage,
consistency and smoothness.
In particular the consistency criterion used to measure the homogeneity property plays a
key role in the segmentation process. For this reason, we redefine consistency making GeneSIS
capable of handling the ambiguous areas of the image more effectively. In addition to the
aforementioned algorithmic improvements, we also incorporate three further extensions involved
in different parts of our classification configuration, based on existing approaches of the literature.
All these tools are properly formulated, adapting to the fuzzy representation principles
characterizing GeneSIS. First, since GeneSIS is a markerdriven algorithm, we suggest a marker
selection strategy to generate more reliable markers, in an attempt to enhance the segmentation
process. The method concentrates on the core (confident) regions of the ground components.
Markers are selected according to the components size and the fuzziness of the contained pixels.
A noteworthy distinction of our approach compared with existing schemes is that the pixels
uncertainty is measured here by considering the difference of fuzzy degrees between the dominant
and the most competing classes. Secondly, in the experimental analysis, we investigate two
GeneSIS-based classification schemes, namely the supervised segmentation and the spectral-
spatial classification methods. In the former case, GeneSIS is applied to fuzzy images acquired via
supervised SVM classification. In the latter case, the initial maps are obtained after fuzzy
clustering. Instead of considering these maps as the final segmentation results as in, here we apply

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GeneSIS to the unsupervised fuzzy maps before spectral-spatial combination. Owing to the
different usage of the combination strategy, we are able to obtain more accurate classification
results. Finally, the third extension aims at incorporating in GeneSIS the fusion principles from
multiple segmentation/classification maps.
This is an effective technique having attracted considerable attention recently in remote
sensing. For instance, a multiscale segmentation framework is developed in by embedding
nonlinear scale-space filtering, whereas suggests a SVM ensemble model that combines multiple
spectral/spatial features at both pixel and object levels. In this context, we exploit the inherent
property of GeneSIS to produce multiple segmentations emanating from different randomizations.
Then, we propose a segmentation fusion approach, where an ensemble of multiple classifications
maps is combined via a fuzzy majority voting rule. The aggregation scheme also incorporates the
certainty degrees to the various classes of extracted segments in the different segmentations.
Experimental evaluation indicates that fusion assures at least best accuracy values from the
ensemble, whereas on the other hand, it improves the quality of classification maps.
The rest of this paper is organized as follows. In Section II, we provide a general description
of the proposed scheme, whereas Section III focuses on the GeneSIS approach. The OEA
algorithm is detailed in Section IV, whereas Section V presents the segmentation fusion scheme.
The University of Pavia image is used as a test bench in Section VI to display the segmentation
results obtained by the proposed scheme and illustrate its properties. Experimental results on three
other images are presented in Section VII. This paper concludes in Section VIII with some final
remarks.

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CONCLUSION:
The newly developed GeneSIS segmentation algorithm has been investigated in this paper
for classification of remotely sensed images. The new framework incorporates significant
additions, including the rotation of BSFs, the marker selection strategy, refinement of the
consistency criterion, supervised/ unsupervised segmentation and segmentation fusion.
Comparative analysis on four data sets demonstrated that GeneSIS is favorably contrasted against
other segmentation methods of the literature. As a future research, we are considering various
issues to improve GeneSIS further. An observed drawback of the proposed method is the
inadequacy in delineating smoothly more complicated object boundaries. To this end, we are
examining several techniques to confront with this demerit, namely, other powerful representations
of BSFs such as polygonal shapes, the consideration of fine objects as the structural units in
segmentation, and more effective approaches to delineate the active areas of BSFs. These
enhancements preserve the encoding simplicity, but on the other hand allow the creation of more
flexible and smooth objects.

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REFERENCES:
[1] X. Huang and L. Zhang, “An adaptive mean-shift analysis approach for object extraction and
classification from urban hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens., vol. 46, no.
12, pp. 4173–4185, Dec. 2008.
[2] Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, “Segmentation and classification of
hyperspectral images using minimum spanning forest grown from automatically selected
markers,” IEEE Trans. Syst., Man, Cybern., vol. 40, no. 5, pp. 1267–1279, Oct. 2010.
[3] J. Bai, S. Xiang, and C. Pan, “A graph-based classification method for hyperspectral images,”
IEEE Trans. Geosci. Remote Sens., vol. 51, no. 2, pp. 803–817, Feb. 2013.
[4] Y. Tarabalka, J. C. Tilton, J. A. Benediktsson, and J. Chanussot, “A marker-based approach
for the automated selection of a single segmentation from a hierarchical set of image
segmentations,” IEEE J. Sel. Topics Appl. Earth Observ., vol. 5, no. 1, pp. 262–272, Feb. 2012.
[5] J. C. Tilton, Y. Tarabalka, P. M. Montesano, and E. Gofman, “Bestmerge region growing
segmentation with integrated nonadjacent region object aggregation,” IEEE Trans. Geosci.
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[6] Y. Tarabalka and J. C. Tilton, “Spectral-spatial classification of hyperspectral images using
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[7] Y. Tarabalka and J. C. Tilton, “Best merge region growing with integrated probabilistic
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Classification of remotely sensed images using the gene sis fuzzy segmentation algorithm

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Classification of remotely sensed images using the gene sis fuzzy segmentation algorithm