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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

[email protected]

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





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





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).





Figure 3: Brovery image fusion.

Figure 4: Multiplicative Image fusion.

Figure 5: PCA Image Fusion.





Figure 6: Wavelet HIS Transformation.

Figure 7: Wavelet PCA Transformation.

Figure 8: HPF Image fusion.

C. Output of image classification





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.





.

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.





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





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





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.

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