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INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES
Volume 2, No 2, 2011
© Copyright 2010 All rights reserved Integrated Publishing services
Research article ISSN 0976 – 4380
Submitted on September 2011 published on November 2011 602
Image fusion techniques for accurate classification of Remote Sensing data Jyoti Sarup
1, Akinchan Singhai
2
1- Associate Professor, Dept. of Civil Engineering, Maulana Azad National Institute of
Technology, Bhopal
2- Ph.D Scholar, Centre for Remote Sensing and GIS, Dept. of Civil Engineering, Maulana
Azad National Institute of Technology, Bhopal
ABSTRACT
The Image fusion techniques are helpful in providing classification accurately. The satellite
images at different spectral and spatial resolutions with the aid of image processing
techniques can improve the quality of information. Especially image fusion is very helpful to
extract the spatial information from two images of different spatial, spectral and temporal
images of same area. An operation of image analysis such as image classification on fused
images provides better results in comparison of original data. In this paper comparison of
various fusion techniques have been discussed and their accuracies have been evaluated on
their respected classification. LISS III multispectral data and panchromatic data have been
used in this study to demonstrate the enhancement and accuracy assessment of fused image
over the original images using ERDAS imagine software.
Keywords: Image Fusion Techniques, Classification, Accuracy Assessment
1. Introduction
Fusion of multi-sensor image data has become a widely acceptable process because of the
complementary nature of various data sets. While High spatial resolution dataset’s are
necessary for an extraction and accurate description of shapes, features and structures,
whereas high spectral resolution is better used for land cover classification. Hence merging of
these two types of data, to get multi-spectral images with high spatial resolution, is beneficial
for various applications like vegetation, land-use, precision farming and urban studies.
Integration of satellite data of high resolution and of multiple spectral bands with appropriate
processing techniques, make it possible to get optimal result in limited fiscal environment.
This study aims to analyze the potentials of image fusion of multispectral and panchromatic
satellite high ground resolution images and evaluating their significance in infrastructural
classification. Furthermore, the usefulness of the fusion technique has been evaluated by
estimating the percentage of correctly classified pixels for the Non-fused and the fused
images by applying supervised and unsupervised classification
Different methods have been used to merge the IRS PAN (high-spatial resolution) and LISS
III (high-spectral resolution) data for a predominantly Urban infrastructure. The accuracy
assessment for both supervised and unsupervised classification has been applied on both
fused images and original image to find out the optimal result based on the statistical
comparison.
2. Objectives
This study has following objectives
Image fusion techniques for accurate classification of Remote Sensing data
Jyoti Sarup, Akinchan Singhai
International Journal of Geomatics and Geosciences
Volume 2 Issue 2, 2011 603
1. Study the image fusion techniques to extract information about infrastructural wealth
and compare the fused images on statistical parameters to ensure their relevance for
preserving spectral information.
2. Comparison of image classification of fused images at supervised and unsupervised
level of classification and accuracy assessment.
3. Comparison of the classification result to identify the best classification technique for
infrastructural classification.
2.1 Study area
The study area covers BHEL industrial area of Bhopal city falling on the Survey of India
Toposheet 55E/7 & 8, consisting of 770 26’ 16.93’’ to 77
0 27’ 40.83” E longitude and 23
0 14’
16.18” to 230 15’ 22.09” N. This area occupies many infrastructural features like industrial
complex, residential colonies, roads and streets, and natural features such as plantation and
vegetation.
3. Data Used and methodology
Table 1: Data used in this study
Type of sensor Band Resolution (meter) Wavelength (Um)
2 23.5 0.52-0.59 green
3 23.5 0.62-0.68 red LISS III
4 23.5 0.77-0.86 NIR
PAN --- 5.8 1.53-1.70 SWIR
3.1 Image processing
Generally satellite images are diverse in phase and in various other parameters for different
sensors and data which lead to unsatisfactory and less accuracy in result .Thus, the image
processing techniques like image fusion are applied to enhance the output to extract the best
possible information of infrastructure features and their growth pattern.
In this study, the results have been obtained by using image registration, image fusion,
classification, accuracy assessment and auto vectorization techniques.
3.2 Image registration
Image registration of IRS PAN and LISS III has been done to make the image unified to a
same Coordinate system. First LISS III imagery has been registered with the SOI Toposheet
no 55 E/7 & 8, after that PAN image of the same area has been registered. To reduce the
spectrum loss of LISS III image, the nearest neighbor resempling method (Jing, 2008) has
been applied.
3.3 Image Fusion
It is the process of merging several images, acquired by two or more sensors at the same
times, together to form a single image to enhance the information extraction (Shamshad et al.,
2004). The five methods tried for merging were Intensity-Hue-Saturation (HIS), Principal
Image fusion techniques for accurate classification of Remote Sensing data
Jyoti Sarup, Akinchan Singhai
International Journal of Geomatics and Geosciences
Volume 2 Issue 2, 2011 604
Component Analysis (PCA), High Pass Filter (HPF), Brovery, and wavelet technique. IRS
LISS III and PAN data has been selected to generate merge image of the study area for
infrastructural classification and mapping using various image fusion techniques. Data
merging techniques depends on level of information representation- pixel level, feature level
and decision level (Parcharidas & Kaji Tani, 2000). The pixel level fusion method has been
adopted because of least information loss during the fusion process, so the digital
classification accuracy of the pixel level fusion is highest (Zheng, 1999). Pixel level fusion
has following three methods (Rüdenauer & Schmitz 2010).
3.3.1 Statistical methods: PCA
In this method a transformation performed on a multivariate data set with correlated variables
into a data set with new uncorrelated variables (Sadjadi, 2005) The first principal component
of low resolution data is replaced by high resolution data (Shamshad et al., 2004).
3.3.2 Numerical method: multiplicative and brovery
Multiplicative fusion method based on the arithmetic integration of the two raster data set.
Brovery transformation performed on the same spectral range covered by multispectral bands
and pan image.
3.3.3 Colourspace transformation with wavelet decomposition
In this transformation source images first decomposed using the discrete wavelet frame
transform (DWFT), Wavelet coefficients from PAN approximation subband and
multispectral Image detail subbands are then combined together, and the fused image is
reconstructed by performing the inverse DWFT (Shutao Li, 2003). Intensity hue and
saturation with wit wavelet decomposition will helpful in case of preserving spectral and
spatial information.
3.3.4 Evaluation parameters of image fusion
For evaluating image fusion quality, we have selected statistical parameters, the mean and
standard deviation.
3.4 Result of image fusion
The fused image outputs were evaluated based on three characteristic, i.e. statistically,
graphically and by comparing classification accuracy. The visual expressions of various
merged products were also studied. The study could help to grade the suitability of various
merging methods for infrastructural mapping and extraction. All the image processing
operations have been performed using ERDAS IMAGINE 9.1 software and their respective
output images displayed in Figure 1 to 5 as the resulting images obtained by different fusion
techniques, they have strong color shifts with respect to the original image. The mean and the
standard deviations are the statistical parameters has been selected for further analysis and
comparison between fused images with respect to original multispectral image. The statistical
parameters have been displayed in Table 2. The difference in output images shows the impact
of different fusion methods. The wavelet based methods with combination of IHS and
Principal component analysis gave the best optimal result. In image recognition, the wavelet
based fusion methods are most suitable because the spectral and structural characteristics of
Image fusion techniques for accurate classification of Remote Sensing data
Jyoti Sarup, Akinchan Singhai
International Journal of Geomatics and Geosciences
Volume 2 Issue 2, 2011 605
infrastructural features can be identified more accurately for visual interpretation and feature
extraction.
4. Image classification
The classification of fused images is important and gives better result in feature clarity and
extraction. The fused data have been classified in both unsupervised and supervised mode.
The IRS LISS-III multispectral image has been used for urban classification, but it has certain
limitation like its ground resolution is 23.5 meter which cannot be sufficient to identify and
extract the liner infrastructural features. To overcome this problem, the fused images have
been used to perform both supervised and unsupervised classification and comparative
analysis was done. In the supervised classification the training data has been collected from
the study area’s subset and maximum likelihood parametric rule used to classify the study
area into infrastructure and unclassified (rest of the area). Unsupervised classification has
been done using ERDAS ISODATA classification algorithms. In Figure 9 to 18, the
classification result have been displayed. After their classification for each type of fusion the
accuracy assessment has been done for evaluating the accuracy of such different image fusion
techniques and their effectiveness for planning purpose.
4.1 Output of image fusion
Figure 1: LISS III Image of Study Area (23.5 meter resolution).
Figure 2: PAN Image of area (5.8 meter resolution).
Image fusion techniques for accurate classification of Remote Sensing data
Jyoti Sarup, Akinchan Singhai
International Journal of Geomatics and Geosciences
Volume 2 Issue 2, 2011 606
Figure 3: Brovery image fusion.
Figure 4: Multiplicative Image fusion.
Figure 5: PCA Image Fusion.
Image fusion techniques for accurate classification of Remote Sensing data
Jyoti Sarup, Akinchan Singhai
International Journal of Geomatics and Geosciences
Volume 2 Issue 2, 2011 607
Figure 6: Wavelet HIS Transformation.
Figure 7: Wavelet PCA Transformation.
Figure 8: HPF Image fusion.
C. Output of image classification
Image fusion techniques for accurate classification of Remote Sensing data
Jyoti Sarup, Akinchan Singhai
International Journal of Geomatics and Geosciences
Volume 2 Issue 2, 2011 608
Figure 9: Brovery Supervised classified Image.
Figure 10: Multiplicative Supervised classified Image.
Figure 11: PCA Supervised Classified Image.
Figure 12: Wavelet HIS Supervised Classified Image.
Image fusion techniques for accurate classification of Remote Sensing data
Jyoti Sarup, Akinchan Singhai
International Journal of Geomatics and Geosciences
Volume 2 Issue 2, 2011 609
.
Figure 13: Wavelet PCA Supervised Classified Image
Figure 14: Brovery fused unsupervised classified Image.
Figure 15: Multiplicative unsupervised classified Image.
Figure 16: PCA Unsupervised Classified image.
Image fusion techniques for accurate classification of Remote Sensing data
Jyoti Sarup, Akinchan Singhai
International Journal of Geomatics and Geosciences
Volume 2 Issue 2, 2011 610
Figure 17: Wavelet HIS Unsupervised Classified Image.
Figure 18: wavelet PCA Unsupervised Classified Image.
Table 2: Statistical output of image fusion and classification
Original Data PCA Multivariate Brovery Wavelet
Transformation
Avg. Std. Avg. Std. Avg. Std. Avg. Std. Avg. Std.
1 96.610 55.35
7
63.39
3
48.21
4
11605.21
4 8117.648
34.71
9
19.48
5 94.433
55.06
1
2 126.55
3
70.04
3
52.56
7
20.26
0
15862.02
2
10846.91
9 42.80
1415
9
125.99
5
70.15
4
3 127.01
3
77.04
3
45.07
4
19.42
9
15978.60
9
11532.77
0
40.69
0
16.18
1
126.38
8
77.19
5
Table 3: Statistical output of merging technique
Type
Brover
y fused
image
Multiplicative
fused image
PCA
fused
image
Wavelet
PCA
transformati
on
Wavelet
HIS
transformati
on
HPF
Fused
image
Original
image(M
SS)
Total
accuracy 80.00 75.00 65.00 80.00 85.00 80.00 75.00
Kappa
accuracy 0.5960 0.5908 0.300 0.604 0.7059 0.5789 0.4792
Image fusion techniques for accurate classification of Remote Sensing data
Jyoti Sarup, Akinchan Singhai
International Journal of Geomatics and Geosciences
Volume 2 Issue 2, 2011 611
Table 4: Accuracy test of supervised classification of fused and original image
4.2 Accuracy assessment
The accuracy assessment comparison of supervised and unsupervised classification is done
and level of accuracy has been calculated and compared. The comparison of total accuracy
and kappa accuracy for both the classifications shows that wavelet PCA Transformation is
the most appropriate for fusion and is having higher level of accuracy in classification as
shown in Table 3 to 5 with detailed statistical result for all fused images. Higher kappa
values have been obtained in wavelet based method. Overall accuracy can be arranged in
following order Wave HIS. > Wavelet PCA > HPF >Multiplicative > Original > PCA.
5. Conclusion
Image Fusion provides the way to integrate disparate and complementary data to enhance the
information apparent in the images as well as to increase the reliability of the interpretation
(asha et al, 2007). The analysis of fused images and original image gives us an idea about the
fusion algorithms and their different impacts on original data and their relevance to extract
the infrastructure information. Out of all five algorithms wavelet PCA Fusion image has high
integrated frequency information and has a high certainty in extraction of construction in the
study area and it is also found that the unsupervised classification of the fused image has the
best result in comparison of original image and supervised classification to extract the
infrastructural information. These fusion analysis techniques followed by classification and
accuracy assessment gives the quantitative evaluation of infrastructure, and can be applied
successfully to extract other classes and features.
6. References
1 Shamshad, A., Wan Hussain, W.M.A., Mohd Sansui, S.A., (2004), Comparison of
different data fusion approaches for surface features extraction using quick bird
images. Proceeding GIS-IDEAS 2004, Hanoi, Vietnam.
2 Parcharidis, I., Kazi-Tani, L.M., (2000), Landsat TM and ERS data fusion: a
statistical approach evaluation for four different methods. Geosciences and Remote
Sensing Symposium, 2000. Proceedings IGARSS, IEEE 2000 International, 24-28
July 2000, pp 2120 –22.
3 Firooz Sadjadi., (2005), Comparative Image Fusion Analysis. Proceedings of the 2005
IEEE Computer Society Conference on Computer Vision and Pattern Recognition
(CVPR'05) - Workshops, pp. 8, June 20-26, 2005.
Type
Brover
y fused
image
Multiplicati
ve fused
image
PCA
fused
image
Wavelet
PCA
transfor
mation
Wavelet
HIS
transformat
ion
HPF
Fused
image
Origina
l
image(
MSS)
Total
accuracy 80.00 75.00 65.00 95.00 90.00 85.00 75.00
Kappa
accuracy 0.5283 0.4898
0.207
9 0.8980 0.7980 0.6939 0.5098
Image fusion techniques for accurate classification of Remote Sensing data
Jyoti Sarup, Akinchan Singhai
International Journal of Geomatics and Geosciences
Volume 2 Issue 2, 2011 612
4 Li, S.T., Kowk, J.T. and Wang, Y.N., (2002), Using the discrete wavelet frame
transform to merge Landsat TM and SPOT panchromatic images. Information Fusion,
3, pp 17-23.
5 Wu Wenbo, Yao Jing, Kang Tingjun., (2008), Study of Remote Sensing Image Fusion
and Its Application in Image Classification. Proceedings of Commission VII, ISPRS
Congress Beijing 2008.
6 Rahman Atiqure, (2006), “Application of Remote Sensing and GIS Technique for
Urban Environment Management and Development of Delhi, India”. Applied Remote
Sensing for Urban Planning Governance and Sustainability,
http://www.springerlink.com /index/x5w74277j3I13959pdf.
7 Verma Ravindra Kumar, Kumari Sangeeta and Tiwari R.K., (2009), Application of
Remote Sensing and GIS technique for efficient urban planning in India, http://
www.csre.iitb.ac.in/~csre/conf/wp-content /uploads/.../OS4_13.pdf.
8 Asha Das, and K.Revathy., (2007),”A Comparative Analysis of Image Fusion
Techniques for Remote Sensed Images” Proceedings of the World Congress on
Engineering 2007 Vol I, WCE 2007, July 2 - 4, 2007, London, U.K.