Spatial Image Enhancement Techniques for ...

E-ISSN: 2278–179X JECET; June – August-2013; Vol.2.No.3, 707-713.

Journal of Environmental Science, Computer Science and Engineering & Technology An International Peer Review E-3 Journal of Sciences and Technology

Available online at www.jecet.org Environmental Science Research Article

Spatial Image Enhancement Techniques for Determining the Water Quality of Gomti River, Lucknow Shivangi Somvanshi1, Richa Dave2, Renu Dhupper3, Bhawna Dubey4 and P. Kunwar4 1-4 4

Amity Institute of Environmental Sciences, Amity University, Noida (U.P.) India

Scientist – D, Uttar Pradesh - Remote Sensing Application Centre (UP-RSAC), Lucknow (U.P.) India Received: 7 July 2013; Revised: 25 July 2013; Accepted: 29 July 2013

Abstract: The present study demonstrates the use of spatially enhanced IRS LISS III and PAN satellite data for monitoring of water quality in part of Gomti River, Lucknow. The spatial enhancement of different spatial data sets is often used in digital image processing to improve the visual and analytical quality of the data. The spatial image enhancement technique combines the spectral and high spatial resolution information from two different sensors into one image, which has both spectral and high spatial resolution. In order to improve the spatial resolution, the efficiency of six different spatial enhancement techniques viz. Principal Component, Multiplicative, Modified IHS, HPF, Ehlers Fusion and Brovey Transform with standard deviation 5.721, 805.628, 1.567, 14.184, 10.657, 12.254 respectively were examined and evaluated. The Modified IHS spatial enhancement technique with higher standard deviation showing overall best result in compare to others in the analysis of water quality. Keywords: Gomti River, Water Quality, Remote Sensing, LISS III, PAN, Mean, Standard Deviation, Principal Component, Multiplicative, Modified IHS, HPF, Ehlers Fusion and Brovey Transform

JECET; June – August 2013; Vol.2.No.3, 707-713.

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INTRODUCTION Gomti River is an important tributary of Ganga River runs across the major parts of Uttar Pradesh, India, covering nine districts and a distance of approximately 940 km. During its course, Gomti River receives huge quantities of untreated sewage agricultural runoffs brings lot of pesticides, fertilizers, street washouts bringing oil, asphalt, sediments; industrial wastes all of which significantly alter the physicochemical characteristics of its water. Besides the industrial effluents, domestic wastewaters are also discharged into the Gomti River 1. Due to increased pollution levels water quality of the river is deteriorating continuously. Water quality refers to the physical, chemical and biological properties of water. It may be degraded by the presence of wastes, nutrients, microorganisms, pesticides, heavy metals and sediments. Different water quality standards have been developed in order to keep check on the extent of water pollution, and in order to maintain these quality standards. Water quality assessment and apportionment of pollution sources of river is been done using in situ laboratory analysis and multivariate statistical techniques. These traditional techniques were time consuming, costly and reference to sample site only. In contrast, remote sensing technique is an economical way to monitor water quality, because it can monitor large areas in a short time on a repetitive basis. It is also easy to update water quality parameters using remote sensing data, which allows continuous monitoring of water quality. Satellite images are very frequently used in monitoring water quality of surface water. Nowadays there is a wide range of systems that provide images in digital format, and their interpretation into terrestrial attributes is very dependent on their spatial and spectral resolution. As a result of the demand for higher classification accuracy and the need in enhanced positioning precision there is always a need to improve the spectral and spatial resolution of remotely sensed imagery. For most of the systems, panchromatic images typically have higher resolution, while multispectral images offer information in several spectral channels. Resolution merge allows us to combine advantages of both kinds of images by merging them into one. The present study demonstrates the use of spatially enhanced LISS III and PAN data for determining the water quality of Gomti River. Images with high spatial and spectral resolution were fused for water quality mapping through digital analysis and modelling. Main objective of the study was to get best enhanced merged image for the visual interpretation or digital analysis by comparing the results obtained through different merging techniques.

METHODOLOGY Study Area and Site Selection for Sampling: The study area covers Gomti River in Lucknow district of Uttar Pradesh covering the distance of approximately 14 km and lies between 260 50’ 47’’ N latitude 800 56’ 48’’ longitude. Data and Software Used: The following data and software were used for achieving the objectives of study: Satellite data of Indian Remote Sensing Satellite (IRS) P6 LISS III (Path - 100, row - 52, 53) acquired on March 2007 with a resolution of 23.5m and IRS 1D PAN data acquired on March 2008 with a resolution of 5.8m single band in the visible region (0.50-0.75µm) have been used to get high resolution, through resolution merging which was useful in identification and mapping of features easily. Following software was used for image processing and GIS application: 1. ERDAS IMAGINE 9.1 2. Arc GIS 9.2


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Registration / Georeferencing And Projection: Registration is the process of transforming/warping one set of image data (usually called the uncorrected image) to register with a second set of data (usually called the corrected or master image). The basic data generated by remote sensing is subjected to geometric distortion (roll, pitch and yaw). Remotely sensed data cannot be used directly for resource information due to inherent distortions in the image data. For the final use of the image data, it is necessary to correct the image data with respect to the map, which is known as image rectification or georeferencing. Image rectification was performed on the above mentioned images using Erdas - Imagine 9.1 software. The process Georeferencing comprises of two major parts, spatial rectification and spectral rectification. Complete rectification and restoration was done using geometric correction method. Geometric rectification of the image was done by first calculating a polynomial transformation model between the corrected image and the uncorrected image; it was required to identify ground control points (GCPs) which were the common points between the corrected and the uncorrected image. GCPs were well distributed over the entire image area for accurate geometric rectification. Output image was produced by the process of interpolation or re-sampling using the transformation models and input image. Re-sampling to produce geometrically rectified image was carried out using cubic convolution interpolation technique. There are various types of projections available in above mentioned software for satellite data processing. In the present study considers UTM projection WGS – 84. Image Fusion: Image fusion is a tool for integrating a high resolution panchromatic image with a multispectral image, in which the resulting fused image contains both the high resolution spatial information of the panchromatic image and the colour information of the multispectral image. In this study the efficiency of six different spatial enhancement techniques viz. Principal component, Multiplicative, Modified HIS, HPF, Ehlers Fusion and Brovery Transform were examined in order to improve the spatial resolution of high resolution panchromatic image with multispectral image of IRS LISS III.

Figure 1: Flowchart depicts the methodology adopted for multi sensor image fusion.

RESULT AND DISCUSSION Evaluation Of Spatial Image Enhancement Techniques: Figure 2 present the high resolution IRS – P6 LISS III (multispectral) image of March 2007 and IRS – ID PAN (panchromatic) image of March 2008 of the part of the study area. The detail characteristics of the satellite images used in the study area are given in Table 1.


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(a)

(b) Figure 2 (a) IRS – P6 LISS III DATA of March 2007 (b) IRS – ID PAN DATA of March 2008

Table-1: Detail characteristics of the satellite images used in the study area PARAMETERS Spectral bands

PAN 0.50µ - 0.75µ

5.2 - 5.8 (at nadir) Spatial Resolution Swath

63km - 70km (at nadir)

Encoding

6 Bits

LISS III Band 2 → 0.52µ - 0.59µ Band 3 → 0.62µ - 0.68µ Band 4 → 0.77µ - 0.86µ Band 5 → 1.55µ - 1.70µ 21.2m to 23.5 m for B2, B3, B4 63.6m to 70.5m for B5 127km to 141km for B2, B3, B4 133km to 148km for B5 7 Bits

The IRS LISS III and PAN merged image was processed for image enhancement using different techniques such as principle component, multiplicative, brovey transformation, intensity hue saturation (IHS), high pass filter (HPF) and ehler’s fusion. The resulting images obtained using PC analysis; multiplicative approach, Brovery transformation, intensity hue saturation (IHS) and high pass filter (HPF) are shown in Figures 3. The statistical parameters (means and standard deviations) of fused images obtained from each approach are presented in Table 1.


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(a) Principal Component

(b) Multiplicative

(c) Brovey Transform

Analysis

(d) Modified IHS

(e) HPF

(f) Ehlers Fusion

Figure 3: IRS LISS III and PAN merged images using six different techniques Table 1: Statistical Parameters of Fused images

Mean

Principle Multiplicative Brovey Component 50.261 3841.183 15.098

Standard 5.721 Deviation

805.628

1.567

Modified HIS 78.532

HPF 78.77

Ehlers Fusion 68.699

14.184

10.657

12.254

In case of principal component analysis (PC) approach the fusion of panchromatic band has reduced the numerical values of the average and the standard deviation of the original multispectral band data. Lee et al.2 has attempted image enhancement on high spectral resolution remote sensing data by noiseadjustment through Principal component transformation. Roger3 has demonstrated enhancement of image by using Principal component transform action with simple automatic noise adjustment. Siljestrome et al. 4 ; Singh and Harrison5 illustrated the application of Principal Components Analysis to image for the recognition of Feature Configuration. Singh and Kaushal6 have done Principal Components Analysis for the extraction of geomorphological features using Radarsat Data.


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For Brovery transformation, it is found that the statistical parameters of fused multi spectral bands are different from that of the original one because the new information is derived from the ratio of panchromatic band. These parameters of the fused image by multiplicative approach are very much different as the new pixel values are computed simply by multiplication of pixel values of the original multispectral band data and the panchromatic band. Hallada and Cox7 applied Brovey transformation for image sharpening for mixed spatial and spectral resolution satellite data. Output images obtained by HPF and Ehlers fusion are almost similar, though study of water quality parameters by them is not possible. The modified IHS method is a vast improvement over traditional IHS for fusing satellite imagery that differs noticeably in spectral response. It can be used to improve and enhance the utility of high-resolution imagery from various satellites. The method is computationally efficient and can be programmed into most commercial image processing packages. Carper et al.8 used intensityhue- saturation transformations for merging SPOT panchromatic and multispectral image data. Intensity-Hue- Saturation transformation model is one of most often used in merging multi-sensor/ multiresolution data9. The comparison and evaluation of the statistical results and visual analysis of images obtained in different techniques, it was found that modified IHS was the best fusion techniques.

CONCLUSION This study proves the importance of spatial enhancement techniques and evaluation methods that should be consistent and the necessity of a combined method for a quantitative and qualitative assessment of spatial improvement and spectral preservation. The Modified HIS spatial enhancement technique gives best result in compare to others spatial enhancement techniques with best representation of water quality. It is best for spatial merging of high resolution panchromatic IRS – ID PAN and multispectral IRS – P6 LISS III images. Therefore it could be used in digital image analysis, visual interpretation, image mapping and photogrammetric purposes. This image processing techniques is quiet faster and resulted in better identifying, extracting and mapping of urban tree cover in compare to standard image processing/ image classification technique or visual interpretation. It could be used in the water quality monitoring and management programme.

ACKNOWLEDGMENT Authors are thankful to Director, Remote Sensing Applications Centre, Uttar Pradesh, Lucknow for providing necessary facilities and support in carrying out this study.

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K.P.Singh, D. Mohan,V.K.Singh, and A. Malik,‘Studies on distribution and fractionation of heavy metals in Gomti river sediments—a tributary of the Ganges, India’, journal of hydrology,2005, 312,14 -27.

2.

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Y.Singh and A.Kaushal, Extraction of Geomorphologic Features Using Radarsat Data, Photonirvachak, J. Indian Society Remote Sensing, 2006,34, 3, 299-307.

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W.A.Hallada, and S.Cox, Image Sharpening for Mixed Spatial and Spectral Resolution Satellite Systems, Proc. of the 17th International Symposium on Remote Sensing of Environment, 1983,. 1023-1032.

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W.J.Carper, T.M. Lillesand, and R.W.Kiefer, The use of Intensity-Hue- Saturation Transformations for Merging SPOT Panchromatic and Multispectral Image data. Photogrammetric Engineering and Remote Sensing, 1990, 56 ,(4): 459-467.

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H.G.Coskun and N.Musaoglu, The Use of Multi-resolution Analysis and IHS transformation for Merging IRS-1C Panchromatic and SPOT-XS Image Data around Istanbul, Proceedings of the 18th EARSeL Symposium on Operational Remote Sensing for Sustainable Development held at Enschede, Netherlands, from May 11-14, 1998. (Eds: Nieuwenhuis, G.J.A., Vaughan, R.A. and Moleaar, M.) Operational Remote Sensing for Sustainable Development, 1999, 353-356.

*Corresponding Author: Ms. Shivangi Somvanshi; Assistant Professor, Amity Institute of Environmental Sciences, Amity University, Noida.

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