ArticlePDF Available

International Journal of Computing and Network Technology Oil Spill Hyperspectral Data Analysis: Using Minimum Distance and Binary Encoding Algorithms

Authors:

Abstract and Figures

Oil spill calamities have increased, threating maritime ecosystems. This reinforces the need for accurate mapping of oil-spill calamities. The use of hyperspectral classifiers to extract areas of oil spill in a test site was achieved in this work. The paper describes the effects of utilizing a set of hyperspectral image analysis algorithms such as Minimum Distance (MD) and Binary Encoding (BE) algorithms to classify hyperspectral images of oil-spill areas in the Gulf of Mexico using Environment for Visualizing Images software. Hyperspectral image subseting, region of interest and principal component analysis were performed in the preprocessing stage, which is used to reduce the vast amount of data and eliminate redundant data. The paper provides empirical insights on the classification accuracy of hyperspectral images. A confusion matrix is used to determine the accuracy of a classification by comparing a classification result with ground truth information. The overall accuracies were 94.6399% and 88.4422% for the MD and BE algorithms, respectively. Therefore, the two algorithms are accurate for classifying hyperspectral images of the Gulf of Mexico. However, the MD algorithm is more accurate than the BE algorithm.
Content may be subject to copyright.
International Journal of Computing and Network Technology
ISSN (2210-1519)
Int. J. Com. Net. Tech. 5, No. 1 (Jan.-2017)
E-mail: sahr_ar@yahoo.com, azolait@uob.edu.bh or alizolait@gmail.com http://journals.uob.edu.bh
Oil Spill Hyperspectral Data Analysis: Using Minimum
Distance and Binary Encoding Algorithms
Sahar A. El_Rahman1,2 & Ali Hussein Saleh Zolait3
1 Department of Computer Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
2 Electrical Department, Faculty of Engineering-Shoubra, Benha University, Cairo, Egypt
3 Department of Information Systems, University of Bahrain, Sakhir, Kingdom of Bahrain
Received: 11 Aug. 2016, Revised: 10 Dec. 2016, Accepted: 21 Sept. 2016, Published: (1 January 2017)
Abstract: Oil spill calamities have increased, threating maritime ecosystems. This reinforces the need for accurate mapping of oil-
spill calamities. The use of hyperspectral classifiers to extract areas of oil spill in a test site was achieved in this work. The paper
describes the effects of utilizing a set of hyperspectral image analysis algorithms such as Minimum Distance (MD) and Binary
Encoding (BE) algorithms to classify hyperspectral images of oil-spill areas in the Gulf of Mexico using Environment for Visualizing
Images software. Hyperspectral image subseting, region of interest and principal component analysis were performed in the
preprocessing stage, which is used to reduce the vast amount of data and eliminate redundant data. The paper provides empirical
insights on the classification accuracy of hyperspectral images. A confusion matrix is used to determine the accuracy of a
classification by comparing a classification result with ground truth information. The overall accuracies were 94.6399% and
88.4422% for the MD and BE algorithms, respectively. Therefore, the two algorithms are accurate for classifying hyperspectral
images of the Gulf of Mexico. However, the MD algorithm is more accurate than the BE algorithm.
Keywords: Hyperspectral Image, Supervised Classification, PCA, Minimum Distance Algorithm, Binary Encoding Algorithm.
1. INTRODUCTION
The concerned authorities and governments have
found a way to obtain information concerning oil spill
using hyperspectral images, and it is obtained by
capturing, analyzing, and studying images from satellites.
Multispectral and hyperspectral technologies have been
developed for science and research applications. New
applications appear by considering multispectral and
hyperspectral imagery (Sykas et al., 2011; Vagni, 2007).
Hyperspectral images are used in several applications
including food safety and quality, medical sciences,
forensics, and agriculture (Headwall photonics company,
2006). Hyperspectral imaging involves gathering and
processing data from across the electromagnetic spectrum.
The human eye sees visible light in three bands red,
green, and blue (RGB) however; spectral imaging
divides the spectrum into more bands. Hyperspectral
images contain a wealth of data, but interpreting them
requires an understanding of exactly what properties of
the ground materials are being measured, and how they
relate to the measurements recorded by the hyperspectral
sensor (Smith, 2012). Sensors in hyperspectral imaging
systems provide images with a large number of
contiguous spectral channels per pixel. Hence,
information about different materials within a pixel can be
obtained (Bayliss et al., 1997).
Hyperspectral imaging systems acquire images with
an abundance of contiguous wavelengths (usually less
than 10 nm). Dozens or hundreds of images are usually
obtained; hence, every pixel in a hyperspectral image has
its own spectrum over a contiguous wavelength range
(Wu and Sun, 2013). Hyperspectral imagery has the
potential to extract more accurate and detailed information
than that obtained in other cases involving remotely
sensed data (Lugo. et al., 2004).
In this method, no prior knowledge of the sample is
needed because an entire spectrum is assumed at each
iteration and post-processing allows all available
information from the dataset to be mined, which is
regarded as the main advantage of hyperspectral imagery.
Another advantage is the pixel-wise incorporation of a
continuous spectral signature of hundreds of wavelengths
into a two-dimensional image of the object under
inspection. The main disadvantages of this method are the
6 Sahar El_Rahman & Ali Zolait: Oil Spill Hyperspectral Data Analysis: Using Minimum
http://journals.uob.edu.bh
associated cost and complexity. Faster computers,
sensitive detectors, and large data storage spaces are
needed for analyzing hyperspectral data (Bauriegel and
Herppich, 2014).
2. SUPERVISED CLASSIFIER
Image classification is viewed as an important aspect
of remote sensing, image analysis, and pattern recognition
(Abbas and Rydh, 2012). Image classification in remote
sensing involves assigning pixels or the basic units of an
image to classes. It has the potential to gather groups of
identical pixels in remotely sensed data, into classes that
match the informational categories of user interest by
comparing pixels to one another and to those of known
identity (Perumal and Bhaskaran, 2010). There are two
primary approaches in hyperspectral classification:
supervised and unsupervised.
Supervised classification is the process of clustering
pixels into classes based on specified training data.
Training data are groups of pixels that represent areas for
which the information class (land cover, geologic type,
etc.) is already known, as shown in Figure 1 (ENVI,
1999).
Figure 1: Supervised classification

in the image is designated by an analyst, each of which is
a known surface material that symbolizes a desired
spectral class. For each training class, the average spectral
pattern is computed using a classification algorithm, and
then the remaining image cells are assigned to the most
similar class. In unsupervised classification, the algorithm
derives its own spectral class set from a spot sample of the
image cells before performing class assignments (Smith,
2012).
The quality of the training sets is the main factor
affecting the quality of supervised classification. All
Training sets are created with digitized features.
supervised classifications usually involve a succession
of operations that must be followed:
1. Setting the training sites.
2. Extracting signatures.
3. Classifying the image.
Usually, two or three training sites are selected. The
greater number of training sites selected, the more
effective the process. This process assures both accuracy
of classification and correct interpretation of the results.
After the training site areas are digitized, statistical
characterizations of the information are performed. The
specific patterns obtained are called signatures. Finally,
the classification methods are applied (Perumal and
Bhaskaran, 2010). Unlike the unsupervised methods that
do not need to the training data. Unsupervised methods
automatically cluster the data of image into various
groups according to predefined criteria or a cost function
(for example, clustering data based on minimum
distance). These groups are then mapped to classes (Tsot
and Olsenj, 2005).
There are various methods of supervised classification
that have been developed to solve the hyperspectral data
classification problem. In this work, two algorithms the
MD and BE algorithms are applied and their results are
compared.
A. MD Algorithm
The MD decision rule (also called spectral distance
rule) computes the spectral distance between the mean
vector for each signature and the measurement vector for
the candidate pixel. It calculates the mean of the spectral
values for the training set in each band and for each
category, measures the distance from a pixel of unknown
identity to each category, assigns the pixel to the category
with the shortest distance, and denotes a pixel as
     ond the distances defined
by the analyst. Figure 2 is a schematic of the
classification (Al-Ahmadi and Hames, 2007).
Figure 2: Minimum distance (MD) classification
7 Int. J. Com. Net. Tech. 5, No. 1, 5-12 (Jan. 2017)
http://journals.uob.edu.bh
In the MD algorithm:
Each class is represented by its mean vector.
Training is performed using objects (pixels) of a
known class.
The mean of the feature vectors for an object
within a class is calculated.
New objects are classified by determining the
closest mean vector (Center for Image
Analysis), as shown in Figure 3.
Figure 3: Minimum distance to means classification
As with all classification algorithms, every pixel in
the image is analyzed to determine the class assignments.
This can be time consuming depending on the file size.
Hence, the standard MD classification procedure that's
been modified to increase the computational efficiency.
Under a normal MD classification, all the pixels are
assigned to the nearest spectral class; no pixel is left
unassigned.
Some algorithms allow the user to specify a threshold
distance from the class mean, where beyond it a pixel
will not be assigned and hence, remain unclassified (Al-
Ahmadi and Hames, 2007). The Euclidean MD classifier
is simple and computationally fast. It is a linear classifier,
meaning that the decision surfaces are hyperplanes.
The decision function is:
Gi (x) = - r2i (x) = - (x - µi)T(x - µi) (1)
Where x is the n-dimensional pixel vector being
classified, r is a tunable parameter, T distance-threshold
parameter, and i is the n-dimensional mean vector for
class i. The function Gi (x) is evaluated for each class, and
the pixel is assigned to the class with a maximum value
of Gi (x).
The level set function is the signed MD from the pixel
to the curve. This distance, by convention, is regarded as
negative and positive for pixels outside and inside the
contour C, respectively. The level set function of the
closed front C is defined as (Airouche et al., 2009):
(x,y)=d ((x,y),C) (2)
Where d((x,y),C) is the distance from point (x,y) to
the contour C and the minus or plus sign is chosen
depending on whether point (x,y) is outside or inside
interface C, respectively. The interface is represented
tacitly as the zero-th level set (or contour) of this scalar
function (Airouche et al., 2009):
C= {(x,y)/(x,y)=0} (3)
B. BE Algorithm
Binary encoding (BE) is a standard technique for
classifying hyperspectral images. It reduces the amount of
data while conserving as much information as possible
(Xie et al., 2011). It reduces the information of a pixel
(often represented as 8 bits per channel) to 1 or 2 bits per
channel. The basic idea of BE is to compare the albedo of
each pixel in every band with a threshold and then assign

   
 (4)
where S [i] is the code of the i-th band, Xi is the
attribute of the original spectral vector, and T is the
threshold (the mean of the spectral vectors is selected as
the threshold).
The BE classification technique encodes the data and
endmember spectra into 1s and 0s based on whether a
band falls under or above the spectrum mean. It compares
each encoded data spectrum with the encoded reference
spectrum and produces a classified image. All the pixels
are classified to the endmember with the greatest band
number that matches unless the user specifies a minimum
match threshold in which case some pixels may be
unclassified if they do not meet the criteria (ENVI,
2004).
Proposed probability based improved binary encoding
(PIBE) method includes two principal steps. The first
step is to combine all useful information such as texture,
shape, spectra, and height into binary codes, and the
second step is to compute the matching probability
between image objects and target classes according to the
calculated distances between the corresponding binary
codes (Xie et al., 2011& Xie, Huan and Tong, X., 2013).
8 Sahar El_Rahman & Ali Zolait: Oil Spill Hyperspectral Data Analysis: Using Minimum
http://journals.uob.edu.bh
The encoding rule defines how to convert all useful
data into binary codes. The two divisions of the code can
be explained as follows:
i. The spectra: The encoding rule for the spectra
follows the traditional BE method.
ii. Target classes: To be compatible with the input
binary codes, the target classes need to be coded in
the same way. While in principle, all necessary
values can be obtained using training data and
universal cognition.
With the BE rule, the target classes and image objects
are converted to binary codes; each element is presented
by 2L + 5N, a bit-long binary code. Therefore, the binary
codes of hyperspectral image objects are compared with
those of the target class using a similar probability-based
evaluation measure (Xie et al., 2011& Xie, Huan and
Tong, X., 2013).
Binary spectral encoding is advantageous because it is
simple, effective, and is a low-computational-load
hyperspectral analysis method of classifying and
identifying mineral components. Although this method
provides a sound execution, it has some disadvantages,
i.e., it has a low efficiency in some cases due to the high
spatial resolution of modern hyperspectral sensors and
because this method mainly operates on the pixels, the
efficiency sometimes is low (Mazer et al., 1988).
3. METHODOLOGY
The hyperspectral dataset is first preprocessed. The
preprocessing of the hyperspectral dataset includes
determination of the region of interest (ROI) and applying
PCA. The MD and BE supervised classification
algorithms are used in the processing phase.
C. Preprocessing
The region of study is the Gulf of Mexico, USA.
Hyperspectral data were acquired on 06 June, 2010, and
have 360 bands. The test area was at a major oil spill site.
The study region was downloaded from the SpecTIR site
(SpecTIR, 2012). Figure 4a shows an image of the study
area acquired from Google maps and Figure 4b indicates
the dataset that will be analyzed.
(a)
(b)
Figure 4: Study Area
(i) Region Of Interest
An ROI is a part of an image selected either
graphically or by methods such as thresholding. ROIs are
used in supervised classification, but not unsupervised
classification. The ROI selected in this work is shown in
Figure 5.
Figure 5 : Region of interest (ROI)
(ii) Principal Component Analysis
Principal component analysis (PCA) is a data-
analysis technique used to reduce dimensionality. It
reduces the high dimensional vectors to a set of lower
dimensional vectors (Koonsanit et al., 2012). Number of
bands of original dataset is reduced into a number of new
bands that is called the principle components to increase
the covariance and decrease redundancy in order to
achieve lower dimensionality. Researchers use PCA to
determine the best bands for classification, analyze their
contents, and evaluate the correctness of classification
using PCA images, as shown in Figure 6 and Error!
Reference source not found. (Rodarmel and Shan,
2002).
9 Int. J. Com. Net. Tech. 5, No. 1, 5-12 (Jan. 2017)
http://journals.uob.edu.bh
Figure 6: PCA dimension-reduction technique
Figure 7 : Pixel Vector in PCA
Figure 8 shows the image obtained after applying
PCA to the study area.
Figure 8: Image of area of oil spill after applying PCA
D. Processing
The overall aim of the work is to acquire an image of the
study area. Preprocessing is required for the ROI of the
image, and PCA is required to reduce the vast amount of
data and eliminate the redundant data. After
preprocessing, the main processing is performed by
applying the supervised classification algorithms MD
algorithm or BE algorithm. The process is shown in
Figure 9.
Figure 9: Proposed algorithm architecture
10 Sahar El_Rahman & Ali Zolait: Oil Spill Hyperspectral Data Analysis: Using Minimum
http://journals.uob.edu.bh
4. RESULT
The classification result of the study dataset obtained by
using the MD and BE algorithms in the study area is
shown in Figure 10 and Figure 11. A confusion matrix is
used to show the accuracy of a classification by
comparing a classification result with the ground truth
information.
(a) Original image (b) Classified image
Figure 10: MD classification results
(a) Original image (b) Classified image
Figure 11: BE classification results
The confusion matrix is calculated using previously
determined ground truth ROIs. Table 1 shows the
confusion matrix for the MD algorithm and Error!
Reference source not found.Table 2 shows the
confusion matrix for the BE algorithm. The overall
accuracies for t he MD and BE algorithms are
94.6399% and 88.4422%, respectively. Hence, the MD
algorithm performs better classifications for the study
area than the BE algorithm.
5. CONCLUSION
This work has achieved the use of MD and binary
coding classifiers to extract information about oil-spill
areas in a test site, i.e., the Gulf of Mexico in USA. MD
and BE algorithms were analyzed to evaluate their
ability to classify pixels. PCA was used before the
classification process to reduce the dimensionality of
datasets. The overall accuracy of the MD and BE
algorithms was 94.6399% and 88.4422%, respectively.
This indicates that the MD algorithm produces more
accurate results than the BE algorithm.
Table 1 Confusion matrix classification for MD algorithm.
Overall Accuracy = (565/597) 94.6399%
Kappa Coefficient = 0.9173
Ground Truth (Pixels)
Class
Water
Light oil
Dark oil
Total
Unclassified
0
0
0
0
Water [Blue]
128
5
0
133
Light oil [White]
0
214
0
214
Dark oil [Yellow]
0
27
223
250
Total
128
246
223
597
Ground Truth (Percent)
Class
Water
Light oil
Dark oil
Total
Unclassified
0.00
0.00
0.00
0.00
Water [Blue]
100.0
0
2.03
0.00
22.28
Light oil [White]
0.00
86.99
0.00
35.85
Dark oil [Yellow]
0.00
10.98
100.00
41.88
Total
100.00
100.00
100.00
100.00
Overall Accuracy = (528/597) 88.4422%
Kappa Coefficient = 0.8214
Ground Truth (Pixels)
Class
Water
Light oil
Dark oil
Total
Unclassified
0
0
0
0
Water [Blue]
128
1
0
129
Light oil
[White]
0
195
18
213
Dark oil
[Yellow]
0
50
205
255
Total
128
246
223
597
Ground Truth (Percent)
Class
Water
Light oil
Dark oil
Total
Unclassified
0.00
0.00
0.00
0.00
Water [Blue]
100.00
0.41
0.00
21.61
Light oil
[White]
0.00
79.27
8.07
35.68
Dark oil
[Yellow]
0.00
20.33
91.93
42.71
Total
100.00
100.00
100.00
100.00
11 Int. J. Com. Net. Tech. 5, No. 1, 5-12 (Jan. 2017)
http://journals.uob.edu.bh
REFERENCES
Abbas, K. and Rydh
and Segmentation by Using JSEG Segmentation
   Graphics and Signal Processing
IJIGSP, Vol. 4, No. 10, pp. 48-53.
Airouche, M.; Bentabet, L.; and    
Segmentation Using Active Contour Model and Level Set
Method Applied to Detect Oil Spills  
World Congress on Engineering. Vol I, WCE 2009.
Al-Ahmadi, F.S. and Hames, A.S. (2007), "Comparison of Four
Classification Methods to Extract Land Use and Land
Cover from Raw Satellite Images for Some Remote Arid
     Journal of King
Abdulaziz University-Earth Sciences, Vol. 20 No.1, pp.
167-191.
       
Chlorophyll Fluorescence Imaging for Early Detection of
Plant Diseases, with Special Reference to Fusarium spec.
   Journal of Agriculture, Vol. 4 No.
1, pp. 32-57.
Bayliss, J.; Gualtieri, J.; and    
Hyperspectral Data With Independent Component
Analysis", Proceedings of SPIE AIPR Workshop, volume
9.

Rights Reserved, Australia, 2004.
ENVI, (1999) Laboratory Exercises in Image Processing:
Image Classification  
http://www.exelisvis.com/Portals/0/EasyDNNNewsDocum
ents/Repository/Classification.pdf
Headwall Photonics Company
     
http://www.headwallphotonics.com/applications-old/
Koonsani        
Selection for Dimension Reduction in Hyper Spectral
Image Using Integrated Information Gain and Principal
   International Journal
of Machine Learning and Computing, Vol. 2 No. 3, pp.
251-248.
Lugo, W.; Cruz, K.; Carvajal, C.L.; and Rivera, W. (2004),
     
  proceeding of the IASTED
international conference circuits, signals and systems.
Mazer, A.S.; Martin, M.; Lee, M.; and Solomon, J.E. (1988),
 
Remote Sensing of Environment, Vol. 24 No. 1,
pp. 201-210.
Nde, C.E. and James, E. (2013). Assessment of Spectral Angle
Mapper and Binary Encoding in the Quantification of the
Built Environment from Multi-Spectral Landsat Imagery.
New York Science Journal; 6(9), pp. 107-111.
      
    
Journal of computing, Vol. 2 No. 2, pp. 124-129.
       
    
Surveying and Land Information Systems. Vol. 62 No. 2,
pp.115-123.
     ctral Imaging.
   
http://www.microimages.com pp.1-24.
     Geospatial
Solutions. SpecTIR  
http://www.spectir.com/free-data-samples/
Sykas, D.; Karathanassi, V.; Charoula, A. and Polychronis, K.
(2011Oil Spill Mapping Using Hyperspectral Methods
  proceeding of the Tenth International
Conference on the Mediterranean Coastal Environment,
MEDCOAS.
Tsot, B. and Ol     
spatial information into hidden Markov models for
unsupervised image  International Journal
of Remote Sensing, Vol. 26, No. 10, pp. 2114-2133.
Vagni, F. (2007).    and Multispectral
Imaging Technologies. Technical Report -sur-
Seine Cedex, France; North Atlantic Treaty Organization;
The Research and Technology Organisation (RTO) of
NATO.
Wu, D. and Sun, D.-    
hyperspectral imaging technology for food quality and
safety analysis and assessment: A review Part I:
 Innovative Food Science and Emerging
Technologies, Vol. 19, pp. 1-14.
Xie
,
Huan;
Hei., Ch.; Loh., Pet.; Soe., Uwe; Shi, W. (2011).
A New Binary Encoding Algorithm for the Simultaneous
Region-based Classification of Hyperspectral Data and
Digital Surface Models. E. Photogrammetrie,
Fernerkundung, Geoinformation, Schweizerbart'sche,
Verlagsbuchhandlung, Stuttgart, Germany, Vol. (1), pp.
17-33.
Xie
,
Huan;
Tong, X. (2013). A Probability-based improved
binary encoding algorithm for classification of
hyperspectral images", IEEE J. Sel. Topics Appl. Earth
Observ. Remote Sens., Vol. 7, No. 6, pp.2108 -2118.
12 Sahar El_Rahman & Ali Zolait: Oil Spill Hyperspectral Data Analysis: Using Minimum
http://journals.uob.edu.bh
Sahar Abd El_Rahman was born in
Cairo, Egypt, B.Sc. Electronics,
Computer Systems & communication,
Electrical Engineering Department.
Benha University, Shoubra Faculty of
Engineering, Cairo-Egypt. M.Sc. in an
AI Technique Applied to Machine Aided
Translation, Electronic Engineering,
Electrical Engineering Department,
Benha University, Shoubra Faculty of
Engineering, Cairo-Egypt, May2003. PHD. in Reconstruction
of High-Resolution Image from a Set of Low-Resolution
Images, Electronic Engineering, Electrical Engineering
Department, Benha University, Shoubra Faculty of Engineering,
Cairo-Egypt in Jan2008.
She is ASSISTANT PROFESSOR from 2011 till now at
Princess Nourah Bint Abdulrahman University/Department of
Computer Science, College of Computer and Information
System. Also, She is ASSISTANT PROFESSOR from 2008
till now at Electronics & communication, and Computer
Systems, Electrical Engineering Department, Faculty of
Engineering, Shoubra,, Benha University, Cairo, Egypt. She
was a LECTURE in the same location from 2003 and
INSTRUCTOR in the same location in 1998. Her research
interests include computer vision, digital image processing,
Signal processing, information security and cloud computing.
Dr. Sahar A. El_Rahman is a member of IACSIT since 2013.A
member of IAENG since 2011. She is a member of the

Ali Hussein Zolait is Assistant
Professor of MIS in the department of
Information System at college of
Information Technology at the
University of Bahrain. His research
interests are management information
systems (MIS), diffusion of innovation,
security, and e-commerce application
and performance. He was the Stoops
Distinguished Assistant Professor of E-
commerce and Management Information Systems at Graduate
School of Business, University of Malaya, Malaysia. Dr. Zolait
also serves as the Visiting Research Follow at the University of
Malaya. He has developed hundreds of students at all levels-
undergraduate, MBA, MM, Executive Development, and
Doctoral. Dr. Zolait is aprominent scholar and leader in the
field of Innovation Diffusion and Technology Acceptance. He
has published many articles on aspects of information security,
internet banking, mobile application, supply chain integration,
information systems performance in organization, Web maturity
evaluation, information systems, performance analysis and
instructional technologies, and e-commerce application. He has
research published in leading international journals such as
Government Information Quarterly, Behaviour & Information
Technology, Journal of Systems and Information Technology,
and Journal of Financial Services Marketing. He is the Editor-
in-Chief of the International Journal of Technology Diffusion
(IJTD). He is the IEEE Senior member and currently is the
IEEE Secratry for Bahrain section. Dr. Zolait is one of the
Founders & Member of Board of Directors in the Society of
Excellence & Academic Research, Kingdom of Bahrain.
Member of Machine Intelligence Research Labs (MIR Labs),
and program chair for the forth and fifth e-learning conference,
kingdom of Bahrain.
Article
Full-text available
In the last years, oil spill detection by hyperspectral imaging has been transferred from experimental to operational. In this paper, researchers attempted to use and compare four classification approaches for the identification of oil spills. The hyperspectral image classification approaches 'namely' are support vector machine (SVM), parallelepiped, minimum distance (MD) and binary encoding (BE). These approaches used to identify the oil spill areas in both two study areas which are selected as oil-spill areas in the Gulf of Mexico and the Adriatic Sea. The classifiers are applied to the study areas after pre-processing that include the spatial and spectral subset and atmospheric correction. Whereas, the classifiers applied to the full dataset and region of interest (ROI) before and after performing principal component analysis (PCA). The PCA is utilised to eliminate redundant data, reduce the vast amount of information and consequently, decrease the processing times. The findings indicate that the SVM, MD and BE approaches supply a high classification accuracy better than parallelepiped approach using both datasets obtained from both selected region.
Article
Full-text available
In the last years, oil spill detection by hyperspectral imaging has been transferred from experimental to operational. In this paper, researchers attempted to use and compare four classification approaches for the identification of oil spills. The hyperspectral image classification approaches 'namely' are support vector machine (SVM), parallelepiped, minimum distance (MD) and binary encoding (BE). These approaches used to identify the oil spill areas in both two study areas which are selected as oil-spill areas in the Gulf of Mexico and the Adriatic Sea. The classifiers are applied to the study areas after pre-processing that include the spatial and spectral subset and atmospheric correction. Whereas, the classifiers applied to the full dataset and region of interest (ROI) before and after performing principal component analysis (PCA). The PCA is utilised to eliminate redundant data, reduce the vast amount of information and consequently, decrease the processing times. The findings indicate that the SVM, MD and BE approaches supply a high classification accuracy better than parallelepiped approach using both datasets obtained from both selected region.. He is a senior member of IEEE and he is the IEEE Bahrain section Secretary. He is the Editor-in-Chief of the International Journal of Technology Diffusion (IJTD). He is invited as a Guest Speaker to several conferences and seminars. He is selected as External Assessor (examiners) for PhD theses in the field of information systems and e-commerce. He has published three books and more than 40 research papers in information systems and information technology.
Article
Full-text available
In recent years, market pressures have reinforced the demand to solve the problem of an increased occurrence of Fusarium head blight (FHB) in cereal production, especially in wheat. The symptoms of this disease are clearly detectable by means of image analysis. This technique can therefore be used to map occurrence and extent of Fusarium infections. From this perspective, a separate harvest in the field can be taken into consideration. Based on the application of chlorophyll fluorescence and hyperspectral imaging, characteristics, requirements and limitations of Fusarium detection on wheat, both in the field and in the laboratory, are discussed. While the modification of spectral signatures due to fungal infection allows its detection by hyperspectral imaging, the decreased physiological activity of tissues resulting from Fusarium impacts provides the base for CFI analyses. In addition, the two methods are compared in view of their usability for the detection of Fusarium, and different approaches for data analysis are presented.
Article
Full-text available
In this paper, a adopted approach to fully automatic satellite image segmentation, called JSEG, "JPEG image segmentation" is presented. First colors in the image are quantized to represent differentiate regions in the image. Then image pixel colors are replaced by their corresponding color class labels, thus forming a class-map of the image. A criterion for "good" segmentation using this class-map is proposed. Applying the criterion to local windows in the class-map results in the "J-image", in which high and low values corresponding to possible region boundaries and region centers, respectively. A region growing method is then used to segment the image based on the multi-scale J-images. Experiments show that JSEG provides good segmentation and classification results on a variety of images.
Article
Full-text available
The availability of hyperspectral images expands the capability of using image classification to study detailed characteristics of objects, but at a cost of having to deal with huge data sets. This work studies the use of the principal component analysis as a preprocessing technique for the classification of hyperspectral images. Two hyperspectral data sets, HYDICE and AVIRIS, were used for the study. A brief presentation of the principal component analysis approach is followed by an examination of the infor-mation contents of the principal component image bands, which revealed that only the first few bands contain significant information. The use of the first few principal component images can yield about 70 percent correct classification rate. This study suggests the benefit and efficiency of using the principal component analysis technique as a preprocessing step for the classification of hyperspectral images.
Article
Full-text available
This paper presents a probability-based improved binary encoding algorithm (PIBE) for classification of hyperspectral imagery. In the proposed PIBE method, the spectral, texture and shape information from hyperspectral images as well as height information from digital elevation models (if available) are combined to form a binary code. Based on this, a probability-based approach is further introduced to match the constructed binary code to the corresponding one obtain from target classes (or training data set). Some experiments on a pair of hyperspectral images confirm the effectiveness of the proposed PIBE method.
Article
Full-text available
Remote sensing (RS) technologies was utilized to extract some of the important spatially variable parameters, such as land cover and land use (LCLU), from satellite images for remote arid areas in Saudi Arabia. Four different classification techniques unsupervised (ISODATA), and supervised (Maximum likelihood, Mahalanobis Distance, and Minimum Distance) are applied in three sub-catchments in Saudi Arabia for the classification of the raw TM5 images. The developed maps are then visually compared with each other and accuracy assessments utilizing ground-truths are undertaken. It was found that the Maximum likelihood method gave the best results and both Minimum distance and Mahalanobis distance methods overestimated agriculture land and suburban areas. In spite of missing few insignificant features due to the low resolution of the satellite images (90m), good agreement between parameters extracted automatically from the developed maps and field observations was found.
Article
Unsupervised classification methodology applied to remote sensing image processing can provide benefits in automatically converting the raw image data into useful information so long as higher classification accuracy is achieved. The traditional k‐means clustering scheme using spectral data alone does not perform well in general as far as accuracy is concerned. This is partly due to the failure to take the spatial inter‐pixels dependencies (i.e. the context) into account, resulting in a ‘busy' visual appearance to the output imagery. To address this, the hidden Markov models (HMM) are introduced in this study as a fundamental framework to incorporate both the spectral and contextual information in analysis. This helps generate more patch‐like output imagery and produces higher classification accuracy in an unsupervised scheme. The newly developed unsupervised classification approach is based on observation‐sequence and observation‐density adjustments, which have been proposed for incorporating 2D spatial information into the linear HMM. For the observation‐sequence adjustment methods, there are a total of five neighbourhood systems being proposed. Two neighbourhood systems were incorporated into the observation‐density methods for study. The classification accuracy is then evaluated by means of confusion matrices made by randomly chosen test samples. The classification obtained by k‐means clustering and the HMM with commonly seen strip‐like and Hilbert‐Peano sequence fitting methods were also measured. Experimental results showed that the proposed approaches for combining both the spectral and spatial information into HMM unsupervised classification mechanism present improvements in both classification accuracy and visual qualities.
Article
Hyperspectral (HSI) and multispectral or multiband imaging (MSI) systems are powerful tools in the field of remote sensing. While HSI systems collect at least 100 spectral bands of 10 20 nm width, MSI sensors are systems collecting less than 20, generally non contiguous, spectral bands. HSI systems have a very wide capability of spectral discrimination, while MSI systems are designed to support applications by providing bands that detect information in specific combinations of desirable regions of the spectrum. The number and position of bands in each system provide a unique combination of spectral information and are tailored to the requirements the sensor was designed to support. Promising or well developed military applications of multispectral and hyperspectral technologies are: * Gathering information about battlespace; * Discrimination between targets and decoys; * Defeating camouflage; * Early warning for long range missiles and space surveillance; * Detection of weapons of mass destruction; and * Detection of landmines. The paper reviews today's technologies that are applied in hyperspectral, multispectral and multiband imaging systems and lists commercially available sensors for airborne, spaceborne and ground based applications. Although not exhaustive, the survey does provide a fairly complete picture of all current and emerging technologies and deployed imaging systems. Most HSI and MSI systems work in a wavelength range from the visible to the infrared. This survey is dedicated to the technologies involved in the domain of the infrared, commonly divided in bands called Near Infrared (NIR), Short Wavelength Infrared (SWIR), Medium Wavelength Infrared (MWIR), and Long Wavelength Infrared (LWIR). This paper is part of RTG-33's (SET-065) activities in assessing multispectral/multiband infrared imaging systems. The information in this report is considered valid to a date of September 2005. The information provided is unclassified and publicly available.