12.IJAEST-Vol-No-7-Issue-No-1-Comparison-of-Different-Topographic-Correction-Methods-using-AWiFS-Sat

Comparison of Different Topographic Correction Methods using AWiFS Satellite Data

Sartajvir Singh1

1M-Tech Student, E.C.E Deptt. R.I.E.I.T, Railmajra,

S.B.S. Nagar, Punjab, India [email protected]

Prof. J.K. Sharma2 2Director, Engineering Deptt.

R.I.E.I.T, Railmajra S.B.S. Nagar, Punjab, India [email protected]

Dr. V.D. Mishra3

3Scientist (E), R.S. Research Group SASE, DRDO

Chandigarh, India [email protected]

Abstract: - In general, the topographic effect is particularly evident for steep sloped Himalaya terrain because irregular shape of Himalaya causes variable illumination angles and thus diverse reflection values within one land cover type as low reflectance value in shadow areas and high reflectance value in sun illuminated areas. Therefore, effective removal or minimization of topographic effects is necessary in satellite image data of mountainous regions. Topographic correction methods try to compensate topographically induced illumination variations effect. In this paper, different topographic correction methods such as cosine, C-correction, smooth C-correction, cosine-C, SCS+C, C-Huang Wei, slope matching techniques have analyzed using AWiFS satellite imagery of Himalaya. The performance of different models is evaluated using (1) visual analysis and (2) validation with in

situ observations of spectral reflectance. The objectives of this study are to assess the effectiveness of different topographic corrections on snow cover area of Himalaya terrain. The result shows that slope matching is superb technique as compared to the other topographic correction methods to compensate the effects of variable illumination angles. Keywords: - Topographic correction, Cosine-C, SCS+, Smooth correction, Slope match.

I. INTRODUCTION

The operational use of remote sensing techniques is often obstructed by problems originating from topographic effects on the sensor response. The topographic effect of satellite imagery generally refers to the influence from the apparent intensity of surface reflectivity, which is caused by the solar incidence, terrain slope and viewing angle. Such influences may be augmented when the terrain slope is steeper, especially for mountainous terrains. Due to atmospheric scattering, the sun elevation is also of importance. A surface perpendicular to the sun at a low sun elevation will receive less radiation than a surface perpendicular to the sun at a high solar elevation [1]. In other words, sun-facing illuminated slopes (south aspect) show more reflectance value, whereas the effect is opposite in shaded area (north aspect) show less reflectance value [2]. Differential illumination results in considerable variation in

the spectral characteristics of similar snow and other land covers. Therefore, different topographic correction methods were developed to eliminate or at least reduce the topographic influence.

A number of methods have been developed to correct the effect of topographic variation on satellite images, mainly divided into three main categories (1) Empirical approches such as two stage normalization [3] etc. (2) Lambertains methods such as cosine [3]-[5], cosine-T [4], C-correction [4], cosine-C [3], smooth C [2], SCS+ [6] etc. (3) Non-lambertain methods such as Minnaret [7] etc. It was concluded [8] that physically based cosine correction and SCS correction would overcorrect the shaded area in an image, whereas the correction methods involving experimental parameters such as the C, SCS+C correction perform better and these empherical algorithms have been applied more widely in practice. However it was found that these well develop algorithms are problematic in operation, and some of them cannot perform well enough in some situation [2], [6]. It is reported [9] that slope matching technique superb topographic correction technique in snow cover area of western Himalaya, which was compared with cosine, C-correction, Minneart correction and two stage normalization correction methods. Other topographic correction methods in optical satellite imagery are not investigated very extensively in the Himalayan terrain.

The purpose of this paper was to compare the different

topographic method such as cosine, C-correction, slope matching, smooth C-correction, cosine-C, SCS+C, C-Huang Wei on snow cover area of Himalaya terrain for topographic effects. The results obtained using different topographic methods are compared with the in-situ observations of spectral reflectance. Results suggest that Slope matching is true quantitative retrieval of spectral reflectance, especially in shady area on our study area where as other topographic correction methods overestimate or underestimate the parameters.

Sartajvir Singh* et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 7, Issue No. 1, 085 - 091

ISSN: 2230-7818 @ 2011 http://www.ijaest.iserp.org. All rights Reserved. Page 85

IJAEST

mailto:[email protected]

mailto:[email protected]

II. STUDY AREA

The study area is a part of Lower and Middle Himalaya and shown on AWiFS image (Advance Wide Field Sensor) lies between latitude of 32.254 degree to 32.999 degree North and longitude of 77.00 degree to 77.497 degree East with Azimuth angle 155.69 degree, and Elevation angle 43.79 degree as shown in the Figure 1. The lower part of the area is surrounded by forest and tree line exists up to 3100 m. The upper part (Middle Himalaya) is devoid of forest. The average minimum temperature in winter is generally observed to be -12oC to -15oC in lower Himalaya (Pir-Panjal range) and -30oC to -35oC in Middle Himalaya (Greater Himalaya range). Pir-Panjal receives the highest snowfall (average 15-20 m) as compared to Greater Himalayan range (12-15m) during the winter period between October and May. The altitude in the entire study area varies from 1900 m to 6500 m with a mean value of 4700 m. The slope in the study area varies from 1-86 degree with mean value of 28 degree and aspect ranges from 0-360 degree with mean values of 180 degree. Most of the slopes in the study regions are oriented to south aspect.

III. SATELLITE DATASETS

A cloud free satellite images of AWiFS of 08th January 2009 is used in the present work to study influence of different topographic correction methods. The salient specifications of AWiFS sensor are given in the Table 1.

IV. DIGITAL ELEVATION MODEL GENERATION AND

GEOMETRIC CORRECTION

A master scene of 56m spatial resolution of AWiFS (Advance Wide field sensor) of study area is prepared after rectification with high spatial resolution 23m of LISS-III (Linear Imaging self-Scanning) with 1:50,000 toposheet. A satellite image of AWiFS was than geo-coded with AWiFS to the EVEREST datum by ERDAS/Imagine 9.1 (Leica Geosystems GIS and Mapping LLC) software with sub pixel accuracy. From the DEM dataset, information about the slope, aspect and illumination according to the sun angle and elevation were generated for input to the topographic corrections algorithms. The Pre-processing and Topographic correction steps Shown in Fig.2.

Figure 1 AWiFS image of study area (08th January 2009)

Figure 2 Flow chart of Pre-processing and Topographic correction.

Table 2 Salient Specifications of AWiFS Sensor

Spectral bands

Spectral wavelength

(nm)

Spatial Resolution

(m)

Quantization (bit)

Maximum Radiance

(mw/cm2/sr/μm)

Solar Exoatmostpheric spectral Irradiance

(mw/cm2/sr/μm) B2 520-590 56 10 52.34 185.3281 B3 620-680 56 10 40.75 158.042 B4 770-860 56 10 28.425 108.357 B5 1550-1700 56 10 4.645 23.786

MODIS Images

Geo-referencing

Atmospheric Corrections

DEM

Slope, Aspect

Illumination Angle

Estimation of Reflectance

Estimation of coefficients

Topographically Corrected Reflectance

MODIS Images

Geo-referencing


DEM

Slope, Aspect

Illumination Angle




MODIS Images

Geo-referencing


DEM

Slope, Aspect

Illumination Angle




MODIS Images

Geo-referencing


DEM

Slope, Aspect

Illumination Angle




MODIS Images

Geo-referencing


DEM

Slope, Aspect

Illumination Angle




AWiFS Image

Geo-referencing


DEM

Slope, Aspect

Illumination Angle

Estimation of Reflectance Estimation of coefficients

Apply Different Topographically Corrections method

Different Topographically Corrected Reflectance



IJAEST

V. TOPOGRAPHIC UNCORRECTED REFLECTANCE IMAGE GENERATION

Without topographic consideration, the atmospherically

corrected surface spectral reflectance under lambertian assumption for AWiFS is computed using the following equation [10, 11]:

( )

( ) (1)

Where is the exo-atmospheric spectral irradiance (refer Table1), is the solar zenith angle and calculated for each pixel [12], d is the earth – sun distance in astronomical units and calculated using the approach of [13], is the downwelling diffused radiation and assumed zero according to [14]. is the path radiance which has computed using [14,15].

VI. TOPOGRAPHIC CORRECTION

In this study, different topographic correction methods were analyzed. The methods are the cosine, C-correction, smooth C-correction, cosine-C, SCS+C, C-Huang Wei, slope matching correction techniques as explained in following sections

A. Cosine correction

In this method, the surface is assumed to have Lambertian behavior, i.e. to be a perfect diffuse reflector, having the same amount of reflectance in all view directions. Under the assumption of Lambertian surfaces, the cosine correction [16], [4] has been extensively used to correct for illumination variations [2], [17].

=

(2)

Where is spectral reflectance for horizontal surface, is spectral reflectance observed over the inclined terrain, is solar zenith angle and is illumination (IL) which is calculated using (2), proposed by [3], [18], [2].

( ) (3)

Where is the slope of the surface, aspects of the surface and is the solar azimuth angle.

Although the Lambertian assumption is simple and convenient for topographic correction, there is a recognised problem in the corrected images. Thus when correcting the topographic effect under a Lambertian surface assumption, images tended to be over-corrected, with slopes facing away

from the sun appearing brighter than sun-facing slopes due to diffuse sunlight being relatively more influential on the shady slope.

B. Cosine-C correction

Due to the problem of overcorrection in cosine correction, an improved version has been proposed by [3], which considers the average IL conditions.

= * ( ( )

( ))+ (4)

Where ( ) is mean of illumination of study area.

These models are wavelength independent, since the correction is based on the same factor for all the bands. This assumption is not appropriate as far as diffuse irradiance concerns. Therefore, it should be more appropriate to propose band-dependent factors of topographic correction as per reported in [2].

C. C-correction

Teillet [4] proposed the addition of a semi-empirical moderator (C) to the cosine correction. In this method, “C” is introduced to the cosine correction model as an additive term in (2). C-correction is calculated using (5).

= (

) (5)

Based on an examination of image data, a linear relationship exists between and in the form (6), called regression equation.

(6)

The parameters C are a function of the regression slope (m) and intercept (b)

(7)

The parameter C is said to be analogous to the effects of

diffuse sky irradiance, although the analogy is not exact. The C value, which may also be obtained from the slope and intercept of the regression line from the statistical-empirical approach, exerts a moderating influence on the cosine correction by increasing the denominator and reducing the overcorrection of faintly illuminated pixels. The C-correction method has been shown to retain the spectral characteristics of the data and improve overall classification accuracy in areas of rugged terrain, and it can be derived easily [2], [17].



IJAEST

D. Smooth C-correction

It is reported [2] that best results can be obtained with a variation of the C method, which takes into account the overcorrection of low illuminated slopes by the original C method. Smooth C is a variation of C-correction method based on a smoothed IL value. Most methods produce an overcorrection in those pixels where IL is low. Therefore, a variation in the calculation of the IL was carried out, by smoothing the original slope with a smoothing factor of 3, 5, and 7. Previously slope is calculated using (8),

= arc (

) (8)

This is transformed into (9)

= arc (

) (9)

Where X=3, 5, 7 is a smooth factor. After obtained the slope of corresponding factor, put it in (5) to calculate smooth C-correction. A smoothed correction does not alter reflectance values significantly, whereas a more extreme correction could introduce additional errors.

E. SCS+C : A Modified Sun-Canopy-Sensor Topographic Correction

The SCS correction [19] improves on the cosine

correction by normalizing the illuminated canopy area. It is reported [6] that SCS correction is equivalent to projecting the sunlit canopy from the sloped surface to the horizontal, in the direction of illumination. The cause of the overcorrection in the SCS model is similar to that with the cosine correction. As the angle of incidence approaches 90 degree, the correction factor becomes excessively large. In the C-correction, the parameter C has been shown to have a moderating influence on the cosine correction by emulating the effect of diffuse sky illumination [4], [17]. Soenen [6] proposed the SCS+C correction where the moderator C is derived using (6) and (7) but within the improved physical context of the SCS model. This addition is intended to be an improvement to the SCS correction in a similar way as the C-correction improves on the cosine correction. The formulation for this new SCS+C correction is defined by (10)

= (

) (10)

Where is terrain slope [19] and all other parameter are considered as in [section 2(C)].

F. C-Huang Wei

It is reported [8] that C-Huang Wei method is used for topographic correction under Lambertain methods developed by Huang Wei. It can be calculated using (11).

= ( ) (

)+ (11)

Where minimum value of spectral reflectance, is a minimum value of illumination.

G. Slope match

Nichol and others [20] was proposed slope-matching method who introduced certain modifications to Civco’s (1989) model and considered the topographic corrections in two stages because they observed that Civco’s [3] not provide the well results in shadow areas as per reported in [9]. The final reflectance for topographic correction is estimated using (12) proposed by Nichol and others [20].

( ) (

) (12)

Where

is topographically corrected spectral reflectance, is spectral reflectance on the tilted surface, and is maximum and minimum spectral reflectance and estimated from topographically uncorrected reflectance image, is mean value of illumination on the south aspect. is normalization coefficient for different satellite bands and estimated using equation given in the literature [20].

(13)

Where

is the mean reflectance value on sunlit slopes after first stage normalization, is the mean reflectance value on shady slopes in uncorrected image and

is the mean reflectance value on shady slope after first stage normalization. All parameter required to calculate coefficient is shown in Table 2.

The advantage of this method is that reflectance values in the corrected image are normalized to the mean illumination level of pixels on the sunny aspect rather than the overall mean illumination value of the entire image [20]. The slope-matching method adjusts the brightness between northern and southern slopes. As such, its parameters depend on the image itself as reported in [21].



IJAEST

TABLE 2 PARAMETERS FOR CALCULATION OF (COEFFICIENT) FOR SLOPE MATCHING

Spectral Reflectance’s in AWiFS bands

Parameters Band 2: 520–590 (nm) Band 3: 620–680 (nm) Band 4: 770–860 (nm) Band 5: 1550–1700 (nm) South Aspect (after 1st normalization) 0.824 0.810 0.814 0.139 North Aspect (after 1st normalization) 0.866 0.847 0.830 0.235

North Aspect 0.461 0.442 0.425 0.068 VII. RESULTS AND DISCUSSION

A. Estimation of Coefficient for Topographic Correction

methods

The coefficients for different topographic correction method (C-correction, smooth C with factor 3, smooth C with factor 5, Smooth C with factor 7 and slope match) have shown in Table 3.

B. Model Validation

The results obtained using different topographic models are compared with the in-situ observations of spectral reflectance recorded at the time of satellite pass at Indian

standard time (1130 IST) on 08th January 2009 for model validation. The pixel location of AWiFS imagery of 08th January 2009 was latitude (32.358791o) and longitude (74.119792o). The comparative analysis of different topographic results with in-situ observations in Table 4 shows that some of topographic correction methods overestimate and some of topographic correction methods underestimate but only slope matching method is unique among all the methods and is more suitable for snow cover area of Himalayan terrain. In literature [9], Slope match method performed superb as compare to cosine, C-correction, two stage normalization and Minneart.

All topographic correction methods output images have shown in Fig. 3.

TABLE 3 COEFFICIENTS FOR DIFFERENT TOPOGRAPHIC NORMALIZATION MODELS.

Coefficients for AWiFS imagery of 08th January 2009

C-correction/SCS+C Smooth C (with factor 3) Smooth C (with factor 5) Smooth C (with factor 7) Slope match

16.84 5.990 3.475 2.350 0.8962 15.94 5.679 3.267 2.206 0.9086 15.25 5.396 3.098 2.083 0.9604 16.66 5.529 3.142 2.10 0.4251

TABLE 4 VALIDATIONS OF TOPOGRAPHIC NORMALIZATION MODELS WITH FIELD RESULTS OF SPECTRAL REFLECTANCE USING SPECTRORADIOMETER.

AWiFS – Date: 08th January 2009, pixel location: latitude (32.358791o) and longitude (74.119792o).

Spectral reflectance’s in AWiFS bands Topographic Model Band 2: 520–590 (nm) Band 3: 620–680 (nm) Band 4: 770–860 (nm) Band 5: 1550–1700 (nm)

Cosine-C 0.896 0.895 0.909 0.142 C-correction 0.979 0.978 0.977 0.150

Smooth C (with factor 3) 0.978 0.982 0.976 0.149 Smooth C (with factor 5) 0.982 0.956 0.980 0.151 Smooth C (with factor 7) 0.983 0.957 0.981 0.151

SCS+C 0.979 0.956 0.979 0.149 C-Huang Wei 0.685 0.661 0.663 0.109 Slope match 0.929 0.928 0.923 0.139

Field observation 0.928 0.912 0.909 0.149



IJAEST

Figure 3 Topographic corrected image (a) cosine, (b) cosine-C, (c) C-correction, (d) smooth C with factor 3, (e) smooth C with factor

5, (f) Smooth C with factor 7, (g) SCS+C, (h) C- Huang Wei, (i) slope match.

(a) (b)

(e)

(c)

(f) (d)

(g) (h) (i)



IJAEST

VIII. CONCLUSION In this paper, we have compared different topographic correction methods and concluded that all C methods do not provide well results as compare to slope matching in snow cover area of Himalaya terrain. We observed that slope matching method has advantages in the true quantitative retrieval of spectral reflectance, especially in shady area, compared to C-correction, cosine, cosine-C, smooth C, SCS+C and C-Huang Wei topographic correction methods. Topographic corrections are very useful for further applications as snow cover monitoring, change detection analysis, etc. Further research is needed with imagery on a global basis to derive guidelines on which method performs best under which situation.

ACKNOWLEDGEMENT

The authors would like to thank Director, Snow Avalanche Study establishment, Department of Defence Research and Development Organization.

REFERENCES

[1] Sam Ekstrand, “Landsat TM-Based Forest Damage Assessment: Correction for Topographic Effects”, Photogrammetric Engineering & Remote Sensing, Vol. 62, No. 2, pp. 151-161, 1996.

[2] D. Riano, E. Chuvieco, J. Salas and I. Aguado, “Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types”, IEEE Trans. Geosci. Remote Sens. 41(5) 1056–1061, 2003.

[3] D. L. Civco, “Topographic normalization of Landsat Thematic Mapper digital imagery”, Photogramm. Eng. Remote Sens., vol. 55, pp. 1303–1309, 1989.

[4] P. M. Teillet, B. Guindon, and D. G. Goodenough, “On the slope-aspect correction of multispectral scanner data,” Can. J. Remote Sens., vol. 8, no. 2, pp. 84–106, 1982.

[5] J. Dozier and D. Marks, “Snow mapping and classification from Landsat Thematic Mapper data", Ann. Glaciol., 9, 97–103, 1987.

[6] Scott A. Soenen, Derek R. Peddle, “SCS+C: A Modified Sun-Canopy-Sensor Topographic Correction in Forested Terrain”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 43, No. 9, September 2005.

[7] M. Minnaert,”The reciprocity principle in lunar photometry”, Astrophys. J., 93, 403–410, 1941.

[8] Yongnian Gao; Wanchang Zhang, “A simple empirical topographic correction method for ETM+ imagery”, International Journal of Remote Sensing Volume 30, Issue 9, 2009, Pages 2259 – 2275.

[9] V. D. Mishra, J. K. Sharma, K. K. Singh, N. K. Thakur and M. Kumar, “Assessment of different topographic corrections in AWiFS satellite imagery of Himalaya terrain” J. Earth Syst. Sci. 118, No. 1, pp. 11–26, February 2009.

[10] C. Song, C.E. Woodcock, K.C. Seto, M.P. Lenney and S.A. Macomber, “Classification and change detection using Landsat TM data: when and how to correct atmospheric effects”, Remote Sens. Environ., 75(2), 230–244, 2001.

[11] M.R. Pandya, R.P. Singh, K.R. Murali, P.N. Babu, A.S. Kiran kumar and V.K. Dadhwal, “Bandpass solar exoatmospheric irradiance and Rayleigh optical thickness of sensors on board Indian remote sensing satellites-1B, -1C, -1D, and P4”, IEEE Trans. Geosci. Remote Sens., 40(3), 714–718, 2002.

[12] F. Kasten, “Table of solar altitudes for geographical effect on spectral response from nadir pointing sources. CRREL Spec. Rep. 57, 1962.

[13] F. Van der Meer, “Spectral mixture modelling and spectral stratigraphy in carbonate lithofacies mapping”, ISPRS J. Photogramm. Rem. Sens., 51(3), 150–162, 1996.

[14] P. S. J. Chavez, “Radiometric calibration of Landsat thematic mapper multispectral images’, Photogramm. Eng. Remote Sens., 55(9), 1285–1294, 1989.

[15] P. S. J. Chavez, 1996 Image-based atmospheric corrections revisited and improved; Photogramm. Eng. Remote Sens. 62 1025–1036.

[16] B. Holben and C. Justice, “The topographic effect on spectral response from nadir pointing sources”, Photogramm. Eng. Remote Sens., 46(9), 1191–1200, 1980.

[17] P. Meyer, K. I. Itten, T. Kellenberger, S. Sandmeier, and R. Sandmeier, “Radiometric corrections of topographically induced effects on Landsat TM data in an alpine environment,” ISPRS J. Photogramm. Remote Sens., vol. 48, no. 4, pp. 17–28, 1993.

[18] Y. Kawata, S. Ueno and A. Ohtani, “The surface albedo retrieval of mountainous forest area from satellite MSS data”, Appl. Math. Comput. 69 41–59, 1995.

[19] D. Gu and A. Gillespie, “Topographic normalization of Landsat TM images of forest based on subpixel sun-canopy-sensor geometry,” Remote Sens. Environ., vol. 64, pp. 166–175, 1998.

[20] J. Nichol, L. K. Hang and W. M. Sing, “Empirical correction of low sun angle images in steeply sloping terrain: a slope matching technique”, Int. J. Remote Sens. 27(3–4) 629–635, 2006.



IJAEST

http://www.informaworld.com/smpp/title~db=all~content=t713722504~tab=issueslist~branches=30#v30

http://www.informaworld.com/smpp/title~db=all~content=t713722504~tab=issueslist~branches=30#v30

12.IJAEST-Vol-No-7-Issue-No-1-Comparison-of-Different-Topographic-Correction-Methods-using-AWiFS-Sat

Documents

Transcript of 12.IJAEST-Vol-No-7-Issue-No-1-Comparison-of-Different-Topographic-Correction-Methods-using-AWiFS-Sat