The document discusses the applicability of fuzzy theory in remote sensing image classification. It presents three experiments comparing different classification methods: 1) Unsupervised fuzzy c-means classification, 2) Supervised classification using fuzzy signatures, 3) Supervised classification using fuzzy signatures and membership functions. The supervised fuzzy methods achieved higher accuracy than the unsupervised method, with the third method performing best with an overall accuracy of 83.9%. Fuzzy convolution can further optimize results by combining classification bands.
An introduction to GIS Data Types. Strengths and weaknesses of raster and vector data are discussed. Also covered is the importance of topology. Concludes with a discussion of the vector-based format of OpenStreetMap data.
Gis Geographical Information System FundamentalsUroosa Samman
Gis, Geographical Information System Fundamentals. This presentation includes a complete detail of GIS and GIS Softwares. It will help students of GIS and Environmental Science.
Introduction to GIS - Basic spatial concepts - Coordinate Systems - GIS and Information Systems – Definitions – History of GIS - Components of a GIS – Hardware, Software, Data, People, Methods – Proprietary and open source Software - Types of data – Spatial, Attribute data- types of attributes – scales/ levels of measurements.
it is highly useful for geography students in the field of remote sensing and it is in very simple and explanatory for the purpose of simplification with relevant images in this ppt.
Digital Elevation Model (DEM) is the digital representation of the land surface elevation with respect to any reference datum. DEM is frequently used to refer to any digital representation of a topographic surface. DEM is the simplest form of digital representation of topography. GIS applications depend mainly on DEMs, today.
An introduction to GIS Data Types. Strengths and weaknesses of raster and vector data are discussed. Also covered is the importance of topology. Concludes with a discussion of the vector-based format of OpenStreetMap data.
Gis Geographical Information System FundamentalsUroosa Samman
Gis, Geographical Information System Fundamentals. This presentation includes a complete detail of GIS and GIS Softwares. It will help students of GIS and Environmental Science.
Introduction to GIS - Basic spatial concepts - Coordinate Systems - GIS and Information Systems – Definitions – History of GIS - Components of a GIS – Hardware, Software, Data, People, Methods – Proprietary and open source Software - Types of data – Spatial, Attribute data- types of attributes – scales/ levels of measurements.
it is highly useful for geography students in the field of remote sensing and it is in very simple and explanatory for the purpose of simplification with relevant images in this ppt.
Digital Elevation Model (DEM) is the digital representation of the land surface elevation with respect to any reference datum. DEM is frequently used to refer to any digital representation of a topographic surface. DEM is the simplest form of digital representation of topography. GIS applications depend mainly on DEMs, today.
Presentació realitzada pel Prof. Dr. Thomas H. Kolbe, de l'Institut für Geodäsie, Geoinformatik und Landmanagement de la Universitat Tècnica de Munic, el dia 22/01/2015 a l'ICGC
Study: The Future of VR, AR and Self-Driving CarsLinkedIn
We asked LinkedIn members worldwide about their levels of interest in the latest wave of technology: whether they’re using wearables, and whether they intend to buy self-driving cars and VR headsets as they become available. We asked them too about their attitudes to technology and to the growing role of Artificial Intelligence (AI) in the devices that they use. The answers were fascinating – and in many cases, surprising.
This SlideShare explores the full results of this study, including detailed market-by-market breakdowns of intention levels for each technology – and how attitudes change with age, location and seniority level. If you’re marketing a tech brand – or planning to use VR and wearables to reach a professional audience – then these are insights you won’t want to miss.
MAGNETIC RESONANCE BRAIN IMAGE SEGMENTATIONVLSICS Design
Segmentation of tissues and structures from medical images is the first step in many image analysis applications developed for medical diagnosis. With the growing research on medical image segmentation, it is essential to categorize the research outcomes and provide researchers with an overview of the existing segmentation techniques in medical images. In this paper, different image segmentation methods applied on magnetic resonance brain images are reviewed. The selection of methods includes sources from image processing journals, conferences, books, dissertations and thesis. The conceptual details of the methods are explained and mathematical details are avoided for simplicity. Both broad and detailed categorizations of reviewed segmentation techniques are provided. The state of art research is provided with emphasis on developed techniques and image properties used by them. The methods defined are not always mutually independent. Hence, their inter relationships are also stated. Finally, conclusions are drawn summarizing commonly used techniques and their complexities in application.
Object Classification of Satellite Images Using Cluster Repulsion Based Kerne...IOSR Journals
Abstract: We investigated the Classification of satellite images and multispectral remote sensing data .we
focused on uncertainty analysis in the produced land-cover maps .we proposed an efficient technique for
classifying the multispectral satellite images using Support Vector Machine (SVM) into road area, building area
and green area. We carried out classification in three modules namely (a) Preprocessing using Gaussian
filtering and conversion from conversion of RGB to Lab color space image (b) object segmentation using
proposed Cluster repulsion based kernel Fuzzy C- Means (FCM) and (c) classification using one-to-many SVM
classifier. The goal of this research is to provide the efficiency in classification of satellite images using the
object-based image analysis. The proposed work is evaluated using the satellite images and the accuracy of the
proposed work is compared to FCM based classification. The results showed that the proposed technique has
achieved better results reaching an accuracy of 79%, 84%, 81% and 97.9% for road, tree, building and vehicle
classification respectively.
Keywords:-Satellite image, FCM Clustering, Classification, SVM classifier.
SCAF – AN EFFECTIVE APPROACH TO CLASSIFY SUBSPACE CLUSTERING ALGORITHMSijdkp
Subspace clustering discovers the clusters embedded in multiple, overlapping subspaces of high
dimensional data. Many significant subspace clustering algorithms exist, each having different
characteristics caused by the use of different techniques, assumptions, heuristics used etc. A comprehensive
classification scheme is essential which will consider all such characteristics to divide subspace clustering
approaches in various families. The algorithms belonging to same family will satisfy common
characteristics. Such a categorization will help future developers to better understand the quality criteria to
be used and similar algorithms to be used to compare results with their proposed clustering algorithms. In
this paper, we first proposed the concept of SCAF (Subspace Clustering Algorithms’ Family).
Characteristics of SCAF will be based on the classes such as cluster orientation, overlap of dimensions etc.
As an illustration, we further provided a comprehensive, systematic description and comparison of few
significant algorithms belonging to “Axis parallel, overlapping, density based” SCAF.
International Journal of Computational Engineering Research(IJCER) ijceronline
nternational Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Fuzzy Logic based Edge Detection Method for Image Processing IJECEIAES
Edge detection is the first step in image recognition systems in a digital image processing. An effective way to resolve many information from an image such depth, curves and its surface is by analyzing its edges, because that can elucidate these characteristic when color, texture, shade or light changes slightly. Thiscan lead to misconception image or vision as it based on faulty method. This work presentsa new fuzzy logic method with an implemention. The objective of this method is to improve the edge detection task. The results are comparable to similar techniques in particular for medical images because it does not take the uncertain part into its account.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Geometric Correction for Braille Document Images csandit
Image processing is an important research area in computer vision. clustering is an unsupervised
study. clustering can also be used for image segmentation. there exist so many methods for image
segmentation. image segmentation plays an important role in image analysis.it is one of the first
and the most important tasks in image analysis and computer vision. this proposed system
presents a variation of fuzzy c-means algorithm that provides image clustering. the kernel fuzzy
c-means clustering algorithm (kfcm) is derived from the fuzzy c-means clustering
algorithm(fcm).the kfcm algorithm that provides image clustering and improves accuracy
significantly compared with classical fuzzy c-means algorithm. the new algorithm is called
gaussian kernel based fuzzy c-means clustering algorithm (gkfcm)the major characteristic of
gkfcm is the use of a fuzzy clustering approach ,aiming to guarantee noise insensitiveness and
image detail preservation.. the objective of the work is to cluster the low intensity in homogeneity
area from the noisy images, using the clustering method, segmenting that portion separately using
content level set approach. the purpose of designing this system is to produce better segmentation
results for images corrupted by noise, so that it can be useful in various fields like medical image
analysis, such as tumor detection, study of anatomical structure, and treatment planning.
GAUSSIAN KERNEL BASED FUZZY C-MEANS CLUSTERING ALGORITHM FOR IMAGE SEGMENTATIONcscpconf
Image processing is an important research area in computer vision. clustering is an unsupervised study. clustering can also be used for image segmentation. there exist so many methods for image segmentation. image segmentation plays an important role in image analysis.it is one of the first and the most important tasks in image analysis and computer vision. this proposed system presents a variation of fuzzy c-means algorithm that provides image clustering. the kernel fuzzy
c-means clustering algorithm (kfcm) is derived from the fuzzy c-means clustering algorithm(fcm).the kfcm algorithm that provides image clustering and improves accuracy significantly compared with classical fuzzy c-means algorithm. the new algorithm is called gaussian kernel based fuzzy c-means clustering algorithm (gkfcm)the major characteristic of gkfcm is the use of a fuzzy clustering approach ,aiming to guarantee noise insensitiveness and image detail preservation.. the objective of the work is to cluster the low intensity in homogeneity area from the noisy images, using the clustering method, segmenting that portion separately using content level set approach. the purpose of designing this system is to produce better segmentation results for images corrupted by noise, so that it can be useful in various fields like medical image analysis, such as tumor detection, study of anatomical structure, and treatment planning.
GAUSSIAN KERNEL BASED FUZZY C-MEANS CLUSTERING ALGORITHM FOR IMAGE SEGMENTATIONcsandit
Image processing is an important research area in computer vision. clustering is an unsupervised
study. clustering can also be used for image segmentation. there exist so many methods for image
segmentation. image segmentation plays an important role in image analysis.it is one of the first
and the most important tasks in image analysis and computer vision. this proposed system
presents a variation of fuzzy c-means algorithm that provides image clustering. the kernel fuzzy
c-means clustering algorithm (kfcm) is derived from the fuzzy c-means clustering
algorithm(fcm).the kfcm algorithm that provides image clustering and improves accuracy
significantly compared with classical fuzzy c-means algorithm. the new algorithm is called
gaussian kernel based fuzzy c-means clustering algorithm (gkfcm)the major characteristic of
gkfcm is the use of a fuzzy clustering approach ,aiming to guarantee noise insensitiveness and
image detail preservation.. the objective of the work is to cluster the low intensity in homogeneity
area from the noisy images, using the clustering method, segmenting that portion separately using
content level set approach. the purpose of designing this system is to produce better segmentation
results for images corrupted by noise, so that it can be useful in various fields like medical image
analysis, such as tumor detection, study of anatomical structure, and treatment planning.
Review paper on segmentation methods for multiobject feature extractioneSAT Journals
Abstract Feature extraction and representation plays a vital role in multimedia processing. It is still a challenge in computer vision system to extract ideal features that represents intrinsic characteristics of an image. Multiobject feature extraction system means a system that can extract features and locations of multiple objects in an image. In this paper we have discuss various methods to extract location and features of multiple objects and describe a system that can extract locations and features of multiple objects in an image by implementing an algorithm as hardware logic on a field-programmable gate array-based platform. There are many multiobject extraction methods which can be use for image segmentation based on motion, color intensity and texture. By calculating zeroth and first order moments of objects it is possible to obtain locations and sizes of multiple objects in an image. Keywords: multiobject extraction, image segmentation
GeoAI: A Model-Agnostic Meta-Ensemble Zero-Shot Learning Method for Hyperspec...Konstantinos Demertzis
Deep learning architectures are the most e
ective methods for analyzing and classifying Ultra-Spectral Images (USI). However, e ective training of a Deep Learning (DL) gradient classifier aiming to achieve high classification accuracy, is extremely costly and time-consuming. It requires huge datasets with hundreds or thousands of labeled specimens from expert scientists. This research exploits the MAML++ algorithm in order to introduce the Model-Agnostic Meta-Ensemble Zero-shot Learning (MAME-ZsL) approach. The MAME-ZsL overcomes the above diculties, and it can be used as a powerful model to perform Hyperspectral Image Analysis (HIA). It is a novel optimization-based Meta-Ensemble Learning architecture, following a Zero-shot Learning (ZsL) prototype. To the best of our knowledge it is introduced to the literature for the first time. It facilitates learning of specialized techniques for the extraction of user-mediated representations, in complex Deep Learning architectures. Moreover, it leverages the use of first and second-order derivatives as pre-training methods. It enhances learning of features which do not cause issues of exploding or diminishing gradients; thus, it avoids potential overfitting. Moreover, it significantly reduces computational cost and training time, and it oers an improved training stability, high generalization performance and remarkable classification accuracy.
Mammogram image segmentation using rough clusteringeSAT Journals
Abstract The mammography is the most effective procedure to diagnosis the breast cancer at an early stage. This paper proposes mammogram image segmentation using Rough K-Means (RKM) clustering algorithm. The median filter is used for pre-processing of image and it is normally used to reduce noise in an image. The 14 Haralick features are extracted from mammogram image using Gray Level Co-occurrence Matrix (GLCM) for different angles. The features are clustered by K-Means, Fuzzy C-Means (FCM) and Rough K-Means algorithms to segment the region of interests for classification. The result of the segmentation algorithms compared and analyzed using Mean Square Error (MSE) and Root Means Square Error (RMSE). It is observed that the proposed method produces better results that the existing methods. Keywords— Mammogram, Data mining, Image Processing, Feature Extraction, Rough K- Means and Image Segmentation
Feature Extraction for Image Classification and Analysis with Ant Colony Opti...sipij
The problem of structure extraction from the image which contains many clustered objects is a challenging one for high level image analysis. When an image contains many clustered objects overlapping of objects can cause for hiding the structure. The existing segmentation techniques for better understanding, not able to the address the constituent parts of the image implicitly. The approaches like multistage segmentation address to some extent, but for each stage a separate structure is extracted, and thus causes for the ambiguity about the structure. The proposed approach called Ant Colony Optimization and Fuzzy logic based technique resolves this problem, and gives the implicit structure, that meets with original structure. The segmentation approach uses the swarm intelligence technique based on the behavior of the ant colonies. The segmentation is the process of separating the non-overlapping regions that constitute an image. The segmentation is important for structured and non-structured image analysis and classification for better understanding.
Feature Extraction for Image Classification and Analysis with Ant Colony Opti...
Fuzzy In Remote Classification
1. STUDIA UNIV. BABES–BOLYAI, INFORMATICA, Volume LII, Number 1, 2007
¸
THE APPLICABILITY OF FUZZY THEORY IN REMOTE
SENSING IMAGE CLASSIFICATION
GABRIELA DROJ
Abstract. In the recent years, the usage of Geographic Information Systems
has been rapidly increasing and it became the main tool for analyzing spatial
data in unprecedented number of fields of activities. The evolution of GIS
led to the necessity of faster and better results. The processing time was
reduced by using more and more advanced and applied mathematical and
computer science knowledge. One of these mathematical theories is fuzzy
logic. The fuzzy logic theory gives the possibility of enhancing spatial data
management with the modeling of uncertainty. The usage of fuzzy theory
has also applicability in processing remote sensing data. In this paper is
presented the applicability of fuzzy set theory to the classification of raster
images
1. Introduction
Geographical Information System (shortly GIS) represents a powerful set of
tools for collecting, storing, retrieving at will, transforming and displaying spatial
data from the real world. GIS extends the limits of Computer Aided Design (CAD)
and Automated Mapping (AM) with the possibility of retrieving geospatial data
at request and with the possibility of “what if” analysis and scenarios.
The difference between a CAD system and a GIS system is given by the pos-
sibility of spatial analysis, where new maps are computed from existing ones by
applying either:
• Spatial Join operation between different geospatial data;
• Spatial aggregation;
• Buffer operations, which increase the size of an object by extending its
boundary;
• Set operations, such as complement, union, and intersection.
Received by the editors: July 2007 .
2000 Mathematics Subject Classification. 68U10, 03E72, 03E75.
1998 CR Categories and Descriptors. I.4.6. [Computing Methodologies]: Image Pro-
cessing and Computer Vision – Segmentation; I.5.3.[Computing Methodologies]: Pattern
Recognition – Clustering F.4.1.[Theory of Computation]: Mathematical Logic and Formal
Languages – Mathematical Logic .
89
2. 90 GABRIELA DROJ
In traditional GIS, these operations are exact quantitative operations. Humans,
however, often prefer a qualitative operation over an exact quantitative one, which
can be achieved by extending the standard map, overlaying operations to fuzzy
maps and using fuzzy logic rather than crisp logic.
Fuzzy logic was implemented successfully in various GIS processes like:
• Data collection – analysis and processing remote sensing data for classifi-
cation algorithms and object recognition;
• Spatial analysis - processing qualitative data, defining relationships be-
tween uncertain geospatial objects;
• Complex operations based on genetic algorithms or artificial intelligence;
in this category we can include also the object recognition from airborne images.
Remote sensing from airborne and space borne platforms provides valuable data
for mapping, environmental monitoring, disaster management and civil and mili-
tary intelligence. However, to explore the full value of these data, the appropriate
information has to be extracted and presented in standard format to import it into
geo-information systems and thus allow efficient decision processes. The process
usually used for extracting information from these images is based on classification.
Classification or clustering is the process of automatically grouping a given set
of data into separate clusters such that data points with similar characteristics will
belong to the same cluster. In this way, the number of clusters is reduced. The
process of classification is usually based on object’s attributes or characteristics
than on its geometry but in the process of image classification is based on multi
spectral analysis of the pixels. An image classification is acceptable if the distortion
of the image is minim.
This paper will present the applicability of fuzzy set theory in classification
of raster images. In the same time is presented the possibility to optimize the
classification procedure by using fuzzy set theory to obtain a minimum distortion
of the image.
2. On Classification Methods
In classical cluster analysis each pixel must be assigned to exactly one cluster.
Fuzzy cluster analysis relaxes this requirement by allowing gradual memberships,
thus offering the opportunity to deal with data that belong to more than one
cluster at the same time.
The result of clustering using fuzzy classification consists of a multi-layer output
file, one layer for each cluster. Each layer can be saved as an independent image.
In the image layer, the black is representing the membership 0 and white is repre-
senting the membership 1. The pixels in different gray tones are representing the
degree of membership to the cluster.
The theory of fuzzy sets has its main applicability in the process of raster
classification
3. FUZZY THEORY IN REMOTE SENSING IMAGE CLASSIFICATION 91
2.1. Unsupervised Fuzzy Classification. The unsupervised classification is a
completely automatic process; it’s eliminating the user and in the same time the
influence of known information. This kind of clustering procedure is used where
are no information about the site represented in the image to be classified.[Tur]
The most popular unsupervised classification methods are ISODATA and the
k-means algorithm. [Eas01, Su03] There are many unsupervised classification
algorithms based on fuzzy set theory, we mention fuzzy c-means, fuzzy Gustafson
– Kessel algorithm, fuzzy c - shells and genetic algorithm and so on.[Ben03, Tur].
One of the most popular fuzzy based unsupervised classifications is the Fuzzy
c-means algorithm, similar to K-means algorithm.
2.2. Supervised Classification. The results of classification can be optimized
if there are known information about the image to be clustered. In this case the
method used is called supervised classification. The supervised classification is
made in two steps. The first step is to create signature files (training sites) and
the second step is the classification itself. The fuzzy logic can be used in the
creation of the signatures files and also in the process of the classification itself,
but is not necessary to be used in both stages. [Ben03, Eas01, Liu03, Ste99, Su03]
The signatures files based on this training site can be created using unsuper-
vised classification methods (like ISODATA), classical methods (like Maximum
Likehood or Minim Distance) or by computing a fuzzy matrix filled with the values
indicated by the membership grade of each training site [Eas01]. The membership
degree is computed by using fuzzy membership functions like: piecewise linear
functions, the Gaussian distribution function, the sigmoid curve and quadratic or
cubic polynomial curve. The evaluation on the training can be done using statis-
tical methods: minimum, maximum, mean, and standard deviation for each band
independent and covariance matrix for all the three bands. The most relevant sig-
nature file evaluation is creating an error matrix as a matrix of percentages based
on pixel counts that allows us to see how many pixels in each training sample were
assigned to each class.
The second step of the supervised classification can also be processed with tradi-
tional methods (Minimum distance, Mahalanobis distance or Maximum Likehood)
or by using fuzzy membership functions or genetic algorithm. This stage cannot
be evaluated alone, it can be evaluated just the final result of the supervised clas-
sification.
In order to optimize the accuracy of the results we consider that is appropriate
to use fuzzy logic in both stages of supervised classification. In the next chapter
we are testing these hypotheses.
2.3. Fuzzy convolution. Independently of the classification method used, in or-
der to optimize the results we propose usage of the fuzzy convolution operation.
Fuzzy convolution creates a single classification band by calculating the total
weighted inverse distance of all the classes in a window of pixels and assigning
4. 92 GABRIELA DROJ
the center pixel the class with the largest total inverse distance summed over the
entire set of fuzzy classification bands. This has the effect of creating a context-
based classification to reduce the noise of the classification. Classes with a very
small distance value remain unchanged while classes with higher distance values
may change to a neighboring value if there are sufficient number of neighboring
pixels with class values and small corresponding distance values.
3. Experiments with real-world data
3.1. Input data. For the procedures of image classification was used an orthorec-
tified airborne image from the upper hills of Oradea municipality. This image con-
tains three channels recorded in three bands: the first band for green, the second
for red and the third for blue. In the figure below, we present a fragment of this
image and some statistics for the whole image.
Figure 1. Image fragment and statistics
Statistics Band1 Band 2 Band 3
Min value per cell 9 8 9
Max value per cell 255 255 227
Mean Deviation 127 138 109
Mean 127 137 108
Standard deviation 31.330 22.637 17.801
Correlation 2.7 0.169 0.061
3.2. Definition and verification of the training areas. Training is the first
stage of a supervised classification. In this step the user must define training areas
for each class interactively on the displayed image. The areas may be specified
both on polygon and on pixel basis. The three classes of information defined are:
5. FUZZY THEORY IN REMOTE SENSING IMAGE CLASSIFICATION 93
streets, houses and green areas (figure below). The signature files were created
using a fuzzy membership function based on the sigmoid curve, in this case the
accuracy of the signature files are acceptable, over 90 %( table below).
Figure 2. Training sites
Data Streets Houses Green Areas
Streets 95.45 4.87 1.27
Houses 2.73 93.42 2.83
Green Areas 1.82 1.71 95.89
3.3. Classification procedure. In order to obtain a minimum distortion of the
classified image it was studied the possibility of optimizing the classification pro-
cedure by using fuzzy theory. We analyzed the result of classification procedures
in three experiments.
In the first experiment, the classification procedure is done using unsupervised
classification, by using c-means algorithm. The result of fuzzy c-means algorithm
on the input data, for three clusters is represented in the set of images presented
in figure 3.
In order to optimize the accuracy of the results we consider that is appropriate
to use fuzzy logic in both stages of supervised classification. In the following test
cases, we are testing these hypotheses. In the second case we studied the result
of classification by using fuzzy set theory just for making the signature file and in
the third case we analyzed the results of using fuzzy theory in the both stage of
supervised classification.
The actual classification process can be done by using fuzzy membership func-
tion or based on the standard distance of each pixel to the mean reflectance on
each band for a signature.
On the second experiment we used a method based on the standard distance of
each pixel till the relevant pixel. The result of the clustering on the chosen image,
6. 94 GABRIELA DROJ
Figure 3. Unsupervised Fuzzy Classification - C-means
with the signature file defined before, is represented in figure 4, in three images,
one for each cluster:
• The image in the left for the class representing the houses,
• The image in the middle for the class representing the green areas,
• The image in the right for the class representing the streets
Figure 4. Supervised Fuzzy Classification - First Case
For the third experiment of classifying the input images, with the signature
files defined before, we used a classifying method which combined the Maximum
Likehood method with a fuzzy membership function (linear). The output images
are represented in the figure 5, each image represent a cluster in the following
order houses, grean areas, streets.
3.4. Fuzzy convolution. Classification results can be optimized through fuzzy
convolution. The scope of this procedure is to combine the previously created
bands in one band by interpolation. The interpolation method used is the weight
inverse distance. Applying the fuzzy convolution on the results obtained after the
7. FUZZY THEORY IN REMOTE SENSING IMAGE CLASSIFICATION 95
Figure 5. Supervised Fuzzy Classification - Second Case
second case of supervised classification, the image represented in Figure 6 was
obtained.
Figure 6. Fuzzy Convolution
4. Results of experiments
The results of classification based on fuzzy method were presented in the pre-
vious section, within the figures 1, 2 and 3. As the images show, the supervised
classifications have higher quality than the unsupervised classification. For the
evaluation of the fuzzy classifications presented before, three sets of accuracy mea-
sures have been considered:
• Overall accuracy – based on percent of correct identification;
• Overall kappa coefficient;
• Overall correlation coefficient
8. 96 GABRIELA DROJ
Unsupervised Supervised - first case Supervised - second case
Overall accuracy 34% 72% 83,9%
Kappa 0.1151 0.3421 0.4476
Overall Agreement 0.31 0.69 0.89
5. Conclusion
The major advantage of Fuzzy logic theory is that it allows the natural de-
scription of data and problems, which should be solved, in terms of relationships
between precise numerical values. Fuzzy sets make possible not only the definition
of uncertain, vague or probabilistic spatial data, but also allow relationships and
operations on them.
The usage of fuzzy theory has implications in improving the quality of the
classification of airborne images and object recognition. The Fuzzy set theory of-
fers instruments for supervised and unsupervised classification. The unsupervised
fuzzy based classification allows clustering of data, where no a priori information
known (c-means algorithms), but the supervised classification is offering higher
quality.
The Fuzzy set theory represents a powerful instrument in designing efficient
tools to process remote sensing images and also to support the spatial decision-
making process.
References
[Ben03] Benz, U.; Hofmann, P.; Willhauck, G.; Lingenfelder, I.; Heynen, M. - Multi-resolution,
object-oriented fuzzy analysis of remote sensing data for GIS-ready information, Journal
of Photogrammetry & Remote Sensing, 2004
[Eas01] Eastman, R – Idrisi 32 Release 2, Guide to GIS and Image Processing, Volume I- II,
Clark University, May 2001
[Gue03] Guesgen, H.;Hertzberg, J.; Lobb, R.;Mantler, A. – Buffering Fuzzy Maps in GIS, Spatial
Cognition and Computation, 2003
[Liu03] Liu, H., Li. J., Chapman, M. A.;- Automated Road Extraction from Satellite Imagery
Using Hybrid Genetic Algorithms and Cluster Analysis, Journal of Environmental In-
formatics 1 (2) 40-47 (2003)
[Ste99] Stefanakis, E.; Sellis, T.- Enhancing a Database Management System for GIS with Fuzzy
Set Methodologies, Proceedings of the 19th International Cartographic Conference, Ot-
tawa, Canada, August 1999.
[Su03] Su-Yin Tan - A Comparison of the Classification of Vegetation Characteristics by Spec-
tral Mixture Analysis and Standard Classifiers on Remotely Sensed Imagery within the
Siberia Region, International Institute for Applied Systems Analysis Austria, 2003
[Tur] Turˇan, A.; Ocel´ a, E.; Madar´sz, L. – Fuzzy c-means Algorithms in Remote Sensing,
c ıkov´ a
Technical University of Kosice
City Hall of Oradea, GIS Department