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Gaurav Agrawal*et al / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 4, Issue No. 1, 178 - 186

Comparision of QUAC and FLAASH Atmospheric Correction Modules on EO-1 Hyperion Data of Sanchi Gaurav Agrawal Research Scholar MANIT Bhopal

Prof. Dr. Jyoti Sarup Associate Professor MANIT Bhopal

garvsmarty@gmail.com towards south . The Agricultural fields are mainly the wheat. The climatic conditions are moderate with average rainfall between 900-1000mm annually.

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B. Data sets and methodology – The DataSet properties and the image of study area is as shown in following figure and table. In our data set there were only 158 bands were calibrated and less noisy. C. Software used ENVI 4.7, ERDAS 9.2

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Abstract: The process, which transforms the data from spectral radiance to spectral reflectance, is known as atmospheric correction, compensation, or removal. Hyperion images are the rich source of information contained in hundreds of narrow contiguous spectral bands. There are number of atmospheric agents which contaminate the content of various bands information. To get the complete advantage of Hyperion data it is required to apply atmospheric correction so that the influence of atmosphere on the Earth observation data can be removed. Primary type include scene based and radiation transmission model based algorithms. Major scene based algorithms are IAR and ELM and latest is QUAC. MODTRAN is very popular and effective atmospheric transmission model for correcting mutispectral and hyperspectral data. MODTRAN based FLAASH algorithm available in ENVI is very effective for Hyperion data atmospheric correction. In this paper QUAC and FLAASH available in ENVI has been applied for atmosphere correction of Hyperion data and comparative analysis is carried out.

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Key Words : IAR- Internal Average Reflectance, ELM- Empirical Line Method, MODTRANMODerate resolution atmospheric TRANsmission, FLAASH- Fast Line-of-sight Atmospheric Analysis of Spectral Hypercube, QUAC QUick Atmospheric Correction, SAM- Spectral Angle Mapper

I- Introduction Hyperion sensor is a hyperspectral imager on-board of Earth Observation -1 (EO-1) satellite. The process, which transforms the data from spectral radiance to spectral reflectance, is known as atmospheric correction, compensation, or removal. A. Study areaThe study area is situated 40 kms from Bhopal in north east direction known as Sanchi, in Vidisha district of Madhya Pradesh state. This place is famous for ancient Buddhist Stoops a world monument and Protected by Archeological Department of India as a Place of Heritage. The area of study is mainly constituted by sandstones, Basalts and black cotton soil. The major landforms are hills, of sandstones ISSN:pediments 2230-7818 and pediplane @ 2011 http://www.ijaest.iserp.org. and basalts. The major river is Betwa flowing

Dataset Attribute Entity ID Acquisition Date NW Corner NE Corner SW Corner SE Corner Subset NW Corner Subset NE Corner Subset SW Corner Subset SE Corner Ref. Datum Map Projection Zone No. Image Cloud Cover Receiving Station Scene Start Time Scene Stop Time Date Entered Target Path Target Row Orbit Path Sun Azimuth Sun Elevation Orbit Row Sensor Look Angle Browse Available

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Sanchi dataset EO1H1450442011037110KZ 6th Feb 2011 23.484927N, 77.687076E 23.470050N, 77.758742E 22.637785N, 77.482487E 22.622987N, 77.553704E 23.536851N, 77.699425E 23.535278N, 77.773400E 23.407691N, 77.667750E 23.406135N, 77.741948E WGS84 UTM 43 0 to 9% Cloud Cover SGS 2011 037 05:03:25 2011 037 05:07:44 Feb 6, 2011 145 044 145 140.634988 41.672499 44 4.9418 Y

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Fig. 1 – FCC of Sanchi Area

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Gaurav Agrawal*et al / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 4, Issue No. 1, 178 - 186

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II- Hyperspectral Data- Hyperion

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The data of EO-1 are archived and distributed by the USGS Center for Earth Resources Observation and Science (EROS) and placed in the public domain. There are 242 spectral bands ranging from 356 to 2577 nm. Out of which only 198 bands are calibrated and hence can be used for further processing. The spatial resolution of Hyperion is 30 meter. Each Hyperion scene is collected as a narrow strip, covering a ground area approximately 7.7 km in the across-track direction, and 42 km or 185 km in the along-track direction (depending on the original data acquisition request). The product is distributed by USGS, and the level one product, which is only radiometrically corrected, is available (Pearlman, et al., 2003; USGS, 2004a).

Fig-2 Hyperion’s Swath Width and Length(USGS, 2004 )

of atmospheric agents which contaminate the content of various bands information. To get the complete advantage of Hyperion data it is required to apply atmospheric correction so that some bands which contain useful information and contaminated by atmospheric agents that can be retrieved. The atmospheric correction is often considered as a critical pre-processing step to achieve full spectral information from every pixel especially with hyperspectral data. In hyperspectral image analysis some approaches has been implemented using spectral library or field spactra. If atmospheric correction is not applied then there is markedly difference between observed spectral radiance and spectral library or field spectra. These differences may negatively influence the accuracy to which the image analysis has been carried out based on an independent spectral library or field spectra (Perry E.M et al., 2000).

A. Concepts of Atmospheric Correction Radiation entering a sensor is classified as in Fig. 3. Atmospheric correction is the processing to eliminate S2, S3 and clouds and Gaseous absorption which are contaminating the observed pixels. B. Need of Atmospheric Correction for EO-1 Hyperion Images EO-1 Hyperion hyperspectral images are the rich 2230-7818 http://www.ijaest.iserp.org. source ISSN: of information contained @in2011hundreds of narrow contiguous spectral bands. There are number

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Gaurav Agrawal*et al / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 4, Issue No. 1, 178 - 186

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Fig. 3: Radiation Entering a Sensor Where, S1: Radiance to be observed, S2: Radiance from atmospheric dispersion (Neighboring effect) S3: Path Radiance

III- Atmospheric Correction Approaches Atmospheric correction may be applied by collecting information from scene (image) or by modelling radiation transmission through atmosphere.

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A- Scene Based Empirical Approaches These approaches are based on the radiance values at present in the image (i.e. scene) therefore they are known as scene based empirical approaches. IAR and ELM approaches are commonly used by various researchers. QUAC is an Advance development which is analysed here.

A.1 Internal Average Relative (IAR) Reflectance The Internal Average Relative (IAR) Reflectance approach (Kruse, F.A., 1988) calculates the average spectrum of a scene. The spectrum of any pixel in the scene is then divided by the average spectrum to estimate the relative reflectance spectrum for the pixel. This approach does not need any field measurements of reflectance spectra of surface targets. This approach is mostly applicable for imaging data acquired over arid areas without vegetation.

A.2 QUAC QUAC is a visible-near infrared through shortwave infrared (VNIR-SWIR) atmospheric correction method for multispectral and hyperspectral imagery. Unlike other first principles atmospheric correction methods, ISSN: 2230-7818 @ 2011 http://www.ijaest.iserp.org. it determines atmospheric compensation

parameters directly from the information contained within the scene (observed pixel spectra), without ancillary information. QUAC is based on the empirical finding that the average reflectance of a collection of diverse material spectra, such as the endmember spectra in a scene, is essentially sceneindependent. All of this means significantly faster computational speed compared to the first-principles methods.QUAC also allows for any view or solar elevation angle. Should a sensor not have proper radiometric or wavelength calibration, or the solar illumination intensity be unknown (such as when a cloud deck is present), this approach still allows the retrieval of reasonably accurate reflectance spectra. QUAC provides the following:  Automated atmospheric correction of MSI

and HSI data in the solar reflective spectral region (~0.4-2.5 µm).

 Support for AISA, ASAS, AVIRIS, CAP

ARCHER, COMPASS, HYCAS, HYDICE, HyMap, Hyperion, IKONOS, Landsat TM, LASH, MASTER, MODIS, MTI, QuickBird, RGB, and unknown sensor types. QUAC creates an image of retrieved surface reflectance, scaled into two-byte signed integers using a reflectance scale factor of 10,000. All rights Reserved.

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Gaurav Agrawal*et al / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 4, Issue No. 1, 178 - 186

 Cirrus and opaque cloud classification map  Adjustable spectral polishing

FLAASH supports hyperspectral sensors (such as HyMAP, AVIRIS, HYDICE, HYPERION, Probe1, CASI, and AISA) and multispectral sensors (such as ASTER, IRS, Landsat, RapidEye, and SPOT). Water vapor and aerosol retrieval are only possible when the image contains bands in appropriate wavelength positions . In addition, FLAASH can correct images collected in either vertical (nadir) or slant-viewing geometries.

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IV- Implementation of Atmospheric Correction Algorithms Hyperion data is corrected using various algorithms available in ENVI software. A. QUAC Model The QUAC data processing scheme is outlined below

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B. Radiation Transport Models based approach The scene based approaches like IARR and ELM are not generally producing very good results, as the linearity assumption, which presumes uniform atmospheric transmission, scattering and adjacency effects throughout the scene, may not be accurate. In radiation transport modeling efforts are made to understand and remove the effects of major atmospheric processes with radiation such as absorption and scattering. Very effective and latest atmospheric transmission model is MODerate resolution atmospheric TRANsmission (MODTRAN). MODTRAN is an algorithm and computer model, which is developed by the Air Force Research Laboratory (AFRL) in collaboration with Spectral Sciences, Inc . (SSI). MODTRAN calculates atmospheric transmittance and radiance for frequencies from 0 to 50,000 cm- 1 at moderate spectral resolution of 1 cm- 1. MODTRAN’s internal minimum spectral resolution of 1 cm-1 which corresponds to a spectral resolution of about 0.625 nm at the uppermost wavelength (Ientilucci et al., 2008). The latest model is MODTRAN 4 which is the newly released radiative transfer model which provides accuracy required for the processing of hyperspectral imagery. Therefore FLAASH a MODTRAN 4 based approach is discussed in further sections.

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B.1 Fast Line -of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH)

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FLAASH is a first-principles atmospheric correction tool that corrects wavelengths in the visible through near- infrared and shortwave infrared regions, up to 3 µm. Unlike many other atmospheric correction programs that interpolate radiation transfer properties from a pre-calculated database of modelling results, FLAASH incorporates the MODTRAN4 radiation transfer code. You can choose any of the standard MODTRAN model atmospheres and aerosol types to represent the scene; a unique MODTRAN solution is computed for each image. FLAASH also includes the following features: Correction for the adjacency effect (pixel mixing due to scattering of surface-reflected radiance) 

Fig – 4 Quac Processing QUAC performs a fast and fairly accurate atmospheric correction with the following conditions:  There are at least 10 diverse materials in a

scene.

 There are sufficiently dark pixels in a scene to

allow for a good estimation of the baseline spectrum.

 An option to compute a scene-average visibility

(aerosol/haze amount). FLAASH uses the most advanced techniques for handling particularly stressing atmospheric conditions, such as the presence of 2230-7818 clouds. ISSN: @ 2011 http://www.ijaest.iserp.org.

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Gaurav Agrawal*et al / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 4, Issue No. 1, 178 - 186

V - Analysis

FLAASH Model

A. Spectral Result of Atmospheric correctionThe Atmospheric Correction Module Changes the Reflectance to Radiance and radiance values are changed in to reflectance values. The Application at a point in Study area shows is like this

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This section is a brief overview of the atmospheric correction method used by FLAASH. FLAASH starts from a standard equation for spectral radiance at a sensor pixel, L, that applies to the solar wavelength range (thermal emission is neglected) and flat, Lambertian materials or their equivalents. The equation is as follows:

( 1 )

where:

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ρ is the pixel surface reflectance ρe is an average surface reflectance for the pixel and a surrounding region S is the spherical albedo of the atmosphere La is the radiance back scattered by the atmosphere A and B are coefficients that depend on atmospheric and geometric conditions but not on the surface.

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Each of these variables depends on the spectral channel; the wavelength index has been omitted for simplicity. The first term in Equation (1) corresponds to radiance that is reflected from the surface and travels directly into the sensor, while the second term corresponds to radiance from the surface that is scattered by the atmosphere into the sensor. The distinction between ρ and ρe accounts for the adjacency effect (spatial mixing of radiance among nearby pixels) caused by atmospheric scattering. To ignore the adjacency effect correction, set ρe = ρ. However, this correction can result in significant reflectance errors at short wavelengths, especially under hazy conditions and when strong contrasts occur among the materials in the scene.

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B.

Fig 5- Comparison of radiance to reflectance of plot of same pixel in 3 images a) QUAC corrected b) Original(not corrected) c) FLAASH corrected B. Spectra Comparison of Main ClassesB.1 Vegetation Spectra-

As per Fig -6 Reflectance Properties of Vegetation in the VNIR and SWIR part of the spectrum are dominated by the strong atmospheric absorption regions and absorption properties of the chlorophyll a and b pigments. Narrow absorption at 760 nm corresponding to O2 is compensated by QUAC as well as FLAASH. Pigments in Vegetation show absorption at 640 and 660 nm. Spurious peaks in both the spectra at 940 nm indicated the strong water absorption is under estimated by both models.

The values of A, B, S and La are determined from MODTRAN4 calculations that use the viewing and solar angles and the mean surface elevation of the measurement, and they assume a certain model atmosphere, aerosol type, and visible range.

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Fig – 8 Rock body Spectra

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Fig – 6 Vegetation Spectra B.2 Water Body Spectra-

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Gaurav Agrawal*et al / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 4, Issue No. 1, 178 - 186

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Water bodies have a different response to EMR than water bounded up in molecules in that they do not exhibit discrete absorption features. Water has a high transmittance for all visible wavelengths, but the transmittance increases with decreasing wavelength. However , Suspended materials and pigments cause increased reflectance in visible region(Van Der Meer and De Jong , 2003). In the near infra red and in SWIR all EMR is absorbed by water. FLAASH and ATCOR corrected spectra exhibits spurious spikes in 1900 to 2500 nm wavelengths indication over estimation of water vapour absorption . Similar observations have been reported in SWIR region (Kruse,2003)

Fig -7 Water Body Spectra ISSN: 2230-7818

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B.3 Rock Sample SpectraQuartzites of Sanchi primarily comprises of Quartz, Feldspar, Haematite, Muscovite and other trace minerals like Nontronite, Pyrope, Montmorillonite and minerals of Aluminium and Iron with Sulphur . The Sanchi Quartzites sample was taken for this analysis which contained within one pixel. It is hard and compact exhibiting concoidal fractures when broken with a rock hammer. A megascopic examination of the rock sample shows quartz as the major mineral in the sample in association with minerals like Nontronite and Pyrope. Quartz does not exhibit any significant absorption feature and is considered as featureless spectrum, while minerals such as Montmorillonite and trace mineral Nontronite exhibit absorption feature at 1420 nm and 1915 nm. This absorption feature is seen in the QUAC and FLAASH extracted spectra at the same wavelength. The presence of the absorption feature is checked by Spectral Analyst tool and further presence of this absorption feature is confirmed by the Spectrometer field spectra of quartzite taken from the study area, and is seen in Figure-8 V-Result and Discussion-

A. Matching parameters The spectral angle mapper (SAM) has been widely used as a spectral similarity measure. It calculates spectral similarity between the reference reflectance spectrum (usgs spectral library spectrum) and the test spectrum (image spectrum). The angle between two spectra is used as a measure of discrimination. The result of SAM is an angular difference measured in radian ranging from zero to/2 which gives a qualitative estimate of similarity between image spectrum and spectrometer spectrum (Van der Meer and De Jong, 2003). Small spectral angle values All rights Reserved. Page 183 correspond to high similarity between image spectra


Gaurav Agrawal*et al / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 4, Issue No. 1, 178 - 186

Sample no(pixel values) 1.Rock(751,302) 2.Vegetation(882,266) 3.Water(696,352) 4.Rock(827,393) 5.Rock(823,607) Average

Larger

SAM QUAC 0.0322 0.0966 0.3753 0.2656 0.0504 .1640

angle values

VIConclusion As the SAM comparison shows that there is not a big difference in Spectra matching values for QUAC andFLAASH but FLAASH has an uper hand and give better comparison results but it requires full knowledge of the area , time of flight , sensor elevation etc . QUAC performs a good approximate atmospheric correction to FLAASH or other physicsbased first-principles methods, generally producing reflectance spectra within approximately +/-15% of the physics-based approaches so QUAC can be used for atmospheric corrections of the Hyperspectral images of unknown areas. The future modification and implementation of hyperspectral image and QUAC atmospheric corrected image is in analysis of surface of other planet images as most of them have no atmosphere . The Hyperspectral technique has a good scope of research for extra terrestrial studies.

SAM FLAASH 0.0329 0.0312 0.5175 0.1481 0.0436 .1541

Data and Study AreaB. Spectra Matching ResultsSAM values from different pixel of study area dataset shows that the Sam values of different samples for QUCA and FLAASH are similar average values are lesser for the FLAASH which gives a better result than QUAC. C. Attribute ComparisonAttribute comparison is as per table.

VII – References

Comparison of Attribute Required

FLAASH Hyperion BIL, BIP

QUAC Hyperion BIL, BIP,BSQ

Pixel size Ground elevation

30 0.6 km

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Scene centre Lat/Long

23.05º N, 77.62º E

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Visibility

40 km

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Sensor altitude

703.3166 km

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Flight date & flight time

06/02/2011 05:05:17

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Atmospheric model

Tropical

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Aerosol model Water vapour retrieval Spectral

Rural 1135 nm

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No

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A

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polishing

Yes

Advanced parameters

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Pearlman, J.S., Barry, P.S., Segal, C.C., Shepanski, J., Beiso, D., Carman, S.L., (2003). “Hyperion, a Space Borne Imaging Spectrometer”, IEEE Transactions on Geosciences and Remote Sensing, vol.41, no.6, pp.1160-1173.

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Parameters Sensor type Data Type

Wavelength calibration

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and spectrometer spectra. correspond to less similarity.

Perry E.M., Warner T. and Foote P., (2000), Comparison of atmospheric modeling versus empirical line fitting for mosaicking HYDICE imagery, International Journal of Remote Sensing, vol: 21, no. 4, pp.799-803.

Adler-Golden S., Berk, A, , Bernstein, L.S., Richtsmeier, S., Acharya, P.K., and Matthew, M.W., Aderson, G.P, Allred, C. L., Jeong, L.S., and Chetwynd, J.H.,(2008), “FLAASH, A MODTRAN4 Atmospheric Correction Package for Hyperspectral Data Retrieval and Simulations”. ftp://popo.jpl.nasa.gov/pub/docs/workshops/98_docs/2.p df

Ientilucci E. J., (2008), Using MODTRAN Predicting Sensor-Reaching Radiance, Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, www.cis.rit.edu/~ejipci/Reports/Modtran_lab.pdf

Kruse, F. A., (1988), “ Use of airborne imaging spectrometer data to map minerals associated with hydrothermally altered rocks in the northern Grapevine Mountains, Nevada and California”, Remote Sensing of Environment,vol.24, pp.31-51.

Kruse F. A., (2008), “Comparison of ATREM, ACORN, And FLAASH Atmospheric Corrections using low altitude AVIRIS data of Boulder, Co, USA”, http://www.hgimaging.com/FAK_Pubs.htm.

USGS, 2004a. Earth Observing 1, downloaded on May, 2009, from, url: http://eo1.usgs.gov/

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Gaurav Agrawal*et al / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 4, Issue No. 1, 178 - 186

USGS, 2004b, EO-1, User Guide version 2.3, downloaded on May, 2009, from, eo1.usgs.gov/documents/EO1userguide v2pt320030715UC.pdf

Van der Meer, F., 2004. Analysis of spectral absorption features in hyperspectral imagery. International Journal of Applied Earth Observation and Geoinformation, 5(1): 55-68.

Van der Meer, F. and De Jong, S., 2003. Imaging Spectrometery. Basic Principles and Prospective Applications, 4. Kluwer Achademic Publishers, Dordrecht/ Boston/ London, 35 pp.

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Gaurav Agrawal*et al / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 4, Issue No. 1, 178 - 186

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Thank you

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4.IJAEST-Vol-No-6-Issue-No-2-Comparision-of-QUAC-and-FLAASH-Atmospheric-Correction-Modules-on-EO-1-H