INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 2, No 2, 2011

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1 INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 2, No 2, 2011 Copyright 2010 All rights reserved Integrated Publishing services Research article ISSN 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 Jyoti.sarup@gmail.com ABSTRACT The Image fusion techniques are helpful in providing classification accurately. The satellite s at different spectral and spatial resolutions with the aid of processing techniques can improve the quality of information. Especially fusion is very helpful to extract the spatial information from two s of different spatial, spectral and temporal s of same area. An operation of analysis such as classification on fused s 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 over the original s using ERDAS imagine software. Keywords: Image Fusion Techniques, Classification, Accuracy Assessment 1. Introduction Fusion of multi-sensor 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 s 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 fusion of multispectral and panchromatic satellite high ground resolution s 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 s 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 s and original to find out the optimal result based on the statistical comparison. 2. Objectives This study has following objectives Submitted on September 2011 published on November

2 1. Study the fusion techniques to extract information about infrastructural wealth and compare the fused s on statistical parameters to ensure their relevance for preserving spectral information. 2. Comparison of classification of fused s at supervised and unsupervised level of classification and accuracy assessment.. 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 to E longitude and to N. This area occupies many infrastructural features like industrial complex, residential colonies, roads and streets, and natural features such as plantation and vegetation.. Data Used and methodology Table 1: Data used in this study Type of sensor Band Resolution (meter) Wavelength (Um) green LISS III red NIR PAN SWIR.1 Image processing Generally satellite s 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 processing techniques like 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 registration, fusion, classification, accuracy assessment and auto vectorization techniques..2 Image registration Image registration of IRS PAN and LISS III has been done to make the unified to a same Coordinate system. First LISS III ry has been registered with the SOI Toposheet no 55 E/7 & 8, after that PAN of the same area has been registered. To reduce the spectrum loss of LISS III, the nearest neighbor resempling method (Jing, 2008) has been applied.. Image Fusion It is the process of merging several s, acquired by two or more sensors at the same times, together to form a single to enhance the information extraction (Shamshad et al., 2004). The five methods tried for merging were Intensity-Hue-Saturation (HIS), Principal 60

3 Component Analysis (PCA), High Pass Filter (HPF), Brovery, and wavelet technique. IRS LISS III and PAN data has been selected to generate merge of the study area for infrastructural classification and mapping using various 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, 1). Pixel level fusion has following three methods (Rüdenauer & Schmitz 2010)...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)...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... Colourspace transformation with wavelet decomposition In this transformation source s 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 is reconstructed by performing the inverse DWFT (Shutao Li, 200). Intensity hue and saturation with wit wavelet decomposition will helpful in case of preserving spectral and spatial information...4 Evaluation parameters of fusion For evaluating fusion quality, we have selected statistical parameters, the mean and standard deviation..4 Result of fusion The fused 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 processing operations have been performed using ERDAS IMAGINE.1 software and their respective output s displayed in Figure 1 to 5 as the resulting s obtained by different fusion techniques, they have strong color shifts with respect to the original. The mean and the standard deviations are the statistical parameters has been selected for further analysis and comparison between fused s with respect to original multispectral. The statistical parameters have been displayed in Table 2. The difference in output s 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 recognition, the wavelet based fusion methods are most suitable because the spectral and structural characteristics of 604

4 infrastructural features can be identified more accurately for visual interpretation and feature extraction. 4. Image classification The classification of fused s 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 has been used for urban classification, but it has certain limitation like its ground resolution is 2.5 meter which cannot be sufficient to identify and extract the liner infrastructural features. To overcome this problem, the fused s 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 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 fusion techniques and their effectiveness for planning purpose. 4.1 Output of fusion Figure 1: LISS III Image of Study Area (2.5 meter resolution). Figure 2: PAN Image of area (5.8 meter resolution). 605

5 Figure : Brovery fusion. Figure 4: Multiplicative Image fusion. Figure 5: PCA Image Fusion. 606

6 Figure 6: Wavelet HIS Transformation. Figure 7: Wavelet PCA Transformation. Figure 8: HPF Image fusion. C. Output of classification 607

7 Figure : Brovery Supervised classified Image. Figure 10: Multiplicative Supervised classified Image. Figure 11: PCA Supervised Classified Image. Figure 12: Wavelet HIS Supervised Classified Image. 608

8 Figure 1: Wavelet PCA Supervised Classified Image. Figure 14: Brovery fused unsupervised classified Image. Figure 15: Multiplicative unsupervised classified Image. Figure 16: PCA Unsupervised Classified. 60

9 Figure 17: Wavelet HIS Unsupervised Classified Image. Figure 18: wavelet PCA Unsupervised Classified Image. Table 2: Statistical output of fusion and classification Original Data PCA Multivariate Brovery Wavelet Transformation Avg. Std. Avg. Std. Avg. Std. Avg. Std. Avg. Std Table : Statistical output of merging technique Type Total accuracy Kappa accuracy Brover y fused Multiplicative fused PCA fused Wavelet PCA transformati on Wavelet HIS transformati on HPF Fused Original (M SS)

10 Table 4: Accuracy test of supervised classification of fused and original Type Total accuracy Brover y fused Multiplicati ve fused PCA fused Wavelet PCA transfor mation Wavelet HIS transformat ion HPF Fused Origina l ( MSS) Kappa accuracy 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 to 5 with detailed statistical result for all fused s. 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 s as well as to increase the reliability of the interpretation (asha et al, 2007). The analysis of fused s and original 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 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 has the best result in comparison of original 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 s. 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, Proceedings IGARSS, IEEE 2000 International, July 2000, pp 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,

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