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Sandmeier, St., and K.I. Itten, 1997 1

A PHYSICALLY-BASED MODEL TO CORRECT ATMOSPHERIC AND ILLUMINATION

EFFECTS IN OPTICAL SATELLITE DATA OF RUGGED TERRAIN

St. Sandmeier and K.I. Itten

Remote Sensing Laboratories

Department of Geography

University of Zurich-Irchel CH-8057 Zurich, Switzerland

ABSTRACT

A physically-based model to correct atmospheric and topographically induced illumination

effects in optical satellite data is developed and tested. Special emphasis is put on the impact of

rugged terrain. Ground reference data for various land use classes enables the assessment of

the corrections' influence on land use classifications. The estimation of surface reflectance is

achieved in a two-step procedure. First irradiance components and atmospheric parameters are

calculated for horizontal surfaces using the atmo-code 6S [1], then the influence of the

topography on the parameters is integrated using DEM data.

I. INTRODUCTION

After the launch of the first Landsat satellite in July 1972, scientific studies in remote

sensing primarily focused on land use classification and long-term changes in terrestrial land

cover. In general, a flat terrain was assumed in order to avoid difficulties caused by the

topography. A large fraction of the earth surface, however, consists of mountainous areas

where the impact of topography on remote sensing data has to be examined prior to any remote

sensing application. The interfering effect of topography is evident in a single satellite scene

and introduces even stronger distortions in multi-temporal approaches.

The apparent radiance measured by remote sensing systems in rugged terrain is affected

by (1) the intensity of solar irradiance, (2) the atmospheric effects, (3) the bidirectional

reflectance distribution function (BRDF) of the surface sensed, and (4) the spectral response

functions of the sensor bands. In rugged terrain special emphasis has to be put on the influence

of topography on solar irradiance and on atmospheric effects.

A first approach to correct atmospheric and illumination effects is based on the empirical

relationship between the at-satellite radiance from an object and the direct irradiance provided


by the cosine of the solar incidence angle [2], [3]. The evaluation revealed that these methods

are optimised on a specific satellite scene, test site and object class like forest. Another

drawback is the lack of a solid physical base, which prevents further development of the

empirical and semi-empirical approaches. The aim of a newly initiated ESA study therefore

focused on the physically-based retrieval of surface reflectance [4] following the conditions

listed below:

• atmospheric effects are corrected under consideration of horizontal and vertical variability

• topographically induced variations of the illumination are eliminated taking into account

direct and diffuse irradiance components from sky and terrain

• the methodology is independent from objects, satellite scene and test site; i.e. the

determination of the model parameters is physically-based and makes no use of empirical

data such as the content of a satellite scene

• the bidirectional effects of objects are neglected, assuming Lambertian reflectance

characteristics.

Consequently, objects with identical spectral properties have to reveal the same

reflectance in the satellite image. The three-dimensional relief effect and the atmospheric

blurring have to be suppressed. The satellite image shall appear 'flat' and contrast-enhanced.

The methodology should be applicable to any test site and is not restricted to a specific satellite

sensor. Based on the Lambertian assumption any object class shall be processed. The resulting

target reflectances represent object properties and have to be free from atmospheric and

illumination effects to the fullest possible extent. The radiometrically corrected data shall allow

for multitemporal studies on a multi-sensor basis. Time series of different sensors with similar

spectral bands can be compared because changes in atmosphere and illumination are eliminated,

and the processed imagery represents surface reflectances and not arbitrary digital numbers.

II. DATA BASE

A Landsat TM-scene (frame 194-27) of 11 July 1991, 9:40 am (UT), acquired under

rather hazy atmospheric conditions, is used. At the time of satellite overpass the sun's position

in relation to the test site centre at 47.05° north and 8.52° east is at 33.6° zenith, and 128.3°azimuth. All six reflective TM bands are used.

The test region covering an area of 36.0 km by 17.5 km in the centre of Switzerland

includes the three Swiss Federal Office of Topography Maps 1:25'000 "Zug", "Rigi", and

"Beckenried". The northern part of “Zug” is composed of mainly agricultural areas, water

(Lake of Zug) and settlements. It is characterised by low altitudes between 392 and 1174 m

and a moderate relief. "Rigi" contains large lake areas (Lake of Lucerne) and is dominated by


the Rigi Mountain. Situated in the mountainous pre-Alps, it reveals terrain elevations between

434 m and 1798 m. "Beckenried", in the southern part of the test site, is located partially in

the pre-Alps and in the alpine regions. Here terrain elevation varies from 434 to 2404 m.

Pronounced deep valleys and steep slopes offer a splendid test site for topography-oriented

radiometric corrections.

Radiosonde data at the highest available resolution measured in Payerne by the Swiss

radiosonde station is used to calibrate the atmospheric model for temperature, humidity,

pressure and ozone. Since no aerosol data was available for the time of the satellite overpass,

the 6S-continental aerosol model [1] is used. The estimation of the observed horizontal

visibility is based on meteo-stations "Luzern", "Pilatus", "Altdorf", "Schwyz" and "Engelberg"

which are operated by the Swiss Institute of Meteorology.

A digital elevation model (DEM) with a resolution of 25 m in x and y and 0.1 m for

elevation was available from the Swiss Federal Office of Topography1. It is based on the

1:25'000 Swiss Topographic Maps. Data sets for slope, cosine of incidence angle i, cast

shadow, and sky- and terrain-view factors were derived from this DEM.

Ground reference data for various land use classes was provided by the Swiss Land Use

Statistics 1979/85 of the Swiss Federal Statistical Office. They define land use in 69 different

classes for sample points in a resolution of 100 m for all of Switzerland. The data in the test

site was acquired in 1981. For the classification the original classes have been aggregated into

eight homogenous categories in accordance with the Swiss Federal Statistical Office.

Unfortunately, the Swiss Land Use Statistics do not contain any information on forest stands.

Thus maps of forest stands were digitised in order to assess the influence of radiometric

correction on the classification of forest stands. They had been produced by the Swiss

Sanasilva Project using colour-infrared aerial photographs at a scale of 1:10'000, taken on 25

July 1985 and 13 August 1987. Table 1 gives an overview of the ground reference data used in

the classification.

All data sets used in this study are georeferenced to the rectangular coordinate system of

the Swiss Topographic Maps preceding radiometric corrections. This rectification also includes

geometric correction of relief displacement due to variations in terrain elevation [3]. In order to

avoid introducing new Digital Numbers, a nearest neighbour resampling technique was applied

instead of a bilinear or cubic convolution interpolation.

III. MODEL DESCRIPTION

For a given satellite band b the surface reflectance ρ(b), assuming a Lambertian ground

reflectance, can be calculated by

1 DHM and map data courtesy: Swiss Federal Office of Topography, June 14, 1995


ρ(b) =π L(b) − Lp (b,z)( )E(b,z) Tu (b,z)

(1)

where E(b,z) is the total solar irradiance reaching a surface on altitude z in a given band b;

Tu (b,z) is the upward transmission from the surface to the sensor; Lp (b,z) is the path

radiance from the surface altitude z up to the sensor altitude and L(b) is the scene radiance in

satellite band b.

The derivation of surface reflectance requires firstly the conversion of the digital numbers

(DN) to quantitative physical values. For the six reflective bands of Thematic Mapper the at-

satellite radiances L [mW·cm-2·sr-1·µm-1] are calculated using the TM calibration constants a0

(offset) and a1 (gain) with

L = a0 + a1 ⋅ DN (2)

We used the updated in-flight calibration constants assessed by Slater [5] over the

gypsum sand area of White Sands, New Mexico to convert DN into radiance. To obtain at-

satellite radiance L(b) for a given TM band b, L, as a result of equation (2), has to be

convoluted with the relative spectral response function of each TM band b. They are taken here

from the 6S source code [1] and are based on Markham and Barker [6].

A. The atmosphere module (atmo-module)

The signal reaching a sensor depends on the surface reflectance ρ, but it is perturbed by

two atmospheric processes, the gaseous absorption and the scattering by molecules and

aerosols. The atmospheric code 6S [1] used in this study takes into account Rayleigh and

aerosol scattering, as well as gas absorption due to water (H2O), carbon dioxide (CO2), ozone

(O3), oxygen (O2), methane (CH4), nitrous oxide (N2O), and carbon monoxide (CO) between

0.25 and 4.0 µm in a spectral resolution of 2.5 nm. The input parameters for 6S can be

chosen from proposed standard conditions, or specified by the user. Air pressure, air

temperature, air humidity and ozone concentration in a vertical profile are derived from

radiosonde measurements. Unfortunately, information about type and concentration of aerosols

is usually not gathered in a field campaign although it is of essential importance. The type of

aerosol can be approximated by a standard aerosol model, but optical thickness giving the

concentration of aerosols has to be estimated by horizontal visibilities from meteo-stations or by

the user if no measurement data is available. Horizontal visibilities and optical thickness,

respectively, play a decisive role in atmospheric modelling, thus they have to be carefully deter-

mined.

The 6S code predicts the satellite signal reflected from a plane horizontal surface

assuming cloudless atmosphere. The altitude of the targets is considered. 6S provides all


parameters used in the physically-based model but only for horizontal surfaces. The

topography, except for its altitude, is not considered.

B. The topography module (topo-module)

The path radiance and the upward transmittance Lp (b,z) and Tu (b,z) in equation (3) are

dependent on altitude and spectral conditions only. At the same time however, the total solar

irradiance E(b,z) is strongly affected by the surface orientation. An object lying in shadow

obviously gets less solar irradiance than one exposed to the sun. Furthermore, the geometry

between the sun's position and the surfaces' orientation affects the ratio of direct and diffuse ir-

radiance components, and the amount of terrain reflected radiance reaching an adjacent surface.

Thus this study has to concentrate not only on the altitude dependence of atmospheric effects,

but even more on the impact of topography on the solar irradiance reaching an inclined surface.

The total solar irradiance E(b,z) in a band b for a tilted surface on altitude z consists of

three components: direct, diffuse, and terrain irradiance. Similar to [7] and [8], it is given by:

E(b,z) = Θ ⋅ Edh (b,z) ⋅ cos(i)

cos(sz)+ (direct irradiance)

Efh (b,z) ⋅ k(b,z) ⋅ cos(i)

cos(sz)+ (1 − k(b,z)) ⋅ Vd

+ (diffuse irradiance)

Eh(b,z) ⋅ Vt ⋅ρadj (terrain irradiance)

(3)

where:

E(b,z) = total irradiance on an inclined surface

Eh(b,z) = total irradiance on a horizontal surface

Edh (b,z) = direct component of irradiance on a horizontal surface

Efh (b,z) = diffuse component of irradiance on a horizontal surface

k(b,z) = anisotropy index

Vd = sky-view factor

Vt = terrain-view factorρadj = average reflectance of adjacent objects

Θ = binary coefficient to control cast shadow

i = angle of sun's incidence (cos (i) · 100 = illumination)

sz = solar zenith angle

The first term shows the cosine law applied to direct irradiance Edh (b,z) on a horizontal

surface and results in the amount of direct irradiance on a tilted target. Parameter i is the angle

between the normal on the surface and the sun's rays, thus the angle of incidence of direct


irradiance. Θ is a binary coefficient, and is set to zero for surfaces in cast shadow. Both

parameters i and Θ are derived from the DEM [9].

The second term represents the diffuse irradiance in a sloped terrain. As Proy et al. [10]

recommend, Efh (b,z), the diffuse irradiance on a horizontal surface, is separated into an

isotropic and a circumsolar (anisotropic) component. This becomes necessary as the diffuse

irradiance exhibits a fairly strong anisotropic circumsolar portion which has to be modelled

differently from the isotropic component. On a misty day, it is obvious that there is a peak of

diffuse irradiance in the sun's direction, otherwise the position of the sun could not be detected.

The values of the isotropic and circumsolar components are derived using Hay's [11]

anisotropy index k(b,z) . It is calculated from the ratio of direct irradiance on a surface normal

to the sun's rays Edn (b,z) and the top of the atmosphere radiance Ed

t (b):

k(b,z) = Edn (b,z)

Edt (b)

(4)

k(b,z) is related to the atmospheric transmittance for direct irradiance and values between

0 and 1. It seems to satisfy the wavelength dependence of the scattering process [8]. The lower

the atmospheric transmittance the stronger the isotropic component of the diffuse irradiance and

as a consequence, the lower is k(b,z) .

The circumsolar component of diffuse irradiance can be modelled for topography in the

same way as the direct irradiance Edh (b,z), though it is part of the diffuse irradiance. The

amount of isotropic diffuse irradiance on the other hand, is a function of the proportion of sky

hemisphere not obstructed by topography. Dozier and Marks [12] introduced a sky-view factor

Vd defined as the ratio of the sky portion seen from a specific surface to that on an

unobstructed horizontal surface, i.e. 0 < Vd ≤ 1. The total diffuse irradiance can therefore be

calculated for tilted surfaces by

Ef (b,z) = Efh (b,z) ⋅ k(b,z) ⋅ cos(i)

cos(sz)+ 1 − k(b,z)( ) ⋅ Vd

(5)

anisotropic isotropic

portion portion

where Efh (b,z) and Ef (b,z) are the diffuse irradiance on a horizontal and a tilted surface,

respectively.

The third term in equation (3) refers to terrain irradiance. Especially in the case of deep

valleys, radiance reflected from neighbouring slopes contributes to the irradiance on adjacent

surfaces. The amount of the terrain irradiance depends upon (1) the total irradiance E(b,z)

reaching the adjacent slopes, (2) the portion of adjacent terrain seen from a surface Vt , (3) the

surface reflectances of the adjacent objects ρadj and (4) the distance between the surface sensed

and the adjacent slopes. Thus terrain irradiance has to be accounted for above all in snow


covered rugged terrain. In shadowed areas, however, this effect cannot be neglected even for

dark objects as Vt is large and Ed (b,z) and Ef (b,z) are small [10], [12].

Two approaches are implemented and tested to obtain Vt and Vd , (1) a simplified

trigonometric approach described by Kondratyev [13], and (2) an analytical procedure

introduced by Dozier et al. [14], [15]. The approach of Kondratyev [13] approximates Vt and

Vd by trigonometric functions. The slope angle s of the surface considered is used as the only

parameter to estimate the amount of sky and terrain seen from a point. For a horizontal plane

with slope angle 0° the approach reveals a sky-view factor of 1 and a terrain-view factor of 0,

while for a vertical plane both Vd and Vt turn to 0.5:

Vd = 1 + cos s( )2

(6)

Vt = 1 − cos s( )2

(7)

This simple trigonometric approach can only be applied to a horizontal surface adjoining

an infinitely long slope with slope angle s. The impact of adjacent hills reducing the amount of

visible sky is not considered. The elevation angle s is extracted from the DEM.

The procedure described by Dozier [14], [15] determines Vd and Vt analytically. It

defines first the local horizon points H(i) for each DEM-point i over the entire azimuth circle in

a given resolution ∆θ. Then the local horizon angles h0 are calculated. They represent the

largest slope angle h(i,j) between a DEM-point i and any other DEM-point j in a given direction

θ. Vd and Vt are then obtained by the integration of h over the azimuth circle:

Vd = cos(γ )h 0

π / 2

∫ cos h θ[ ]( )dhdθ0

2π

∫ (8)

Vt = cos(γ )0

h 0

∫ cos h θ[ ]( )dhdθ0

2π

∫ (9)

where:

Vd = sky-view factor

Vt = terrain-view factor

γ = angle between normal on surface and vector with elevation h

θ = azimuth angle

h = elevation angle

h0 = horizon angle

Fig. 1 gives an example of the methodology applied to the sub test site "Beckenried",

pointing out the horizon pixels H(i) for an initial DEM point i with Swiss Map coordinates

676'625 | 201'250. The inset shows the azimuth projection of the corresponding horizon


angles h0 and represents the local horizon line of point i. A resolution of ∆θ = π/16 (32

directions over the azimuth circle) is chosen. The background depicts the integrated surface

within the horizon line of the inset for each DEM-point, which is the sky-view factor Vd .

All algorithms applied in the physically-based model were implemented in a commercially

available image-processing software [9].

IV. RESULTS

A. Visual analysis

In Fig. 2, the radiometrically raw image and the resulting images of the various

radiometric correction steps are shown. To enable a comparison, the images are not processed

by image enhancement techniques except for a linear histogram stretching, applied to all four

images. The radiometrically uncorrected image (Fig. 2a) appears blurred and demonstrates the

hazy atmospheric conditions at the time of satellite overpass. Details in the valley bottom cannot

be distinguished and the topographically induced illumination variations are small, due to the

large amount of blurring diffuse irradiance. In the atmospheric correction (Fig. 2b) the three

TM bands are processed solely using the atmo-module. Thus the only factor corrected is the

altitude dependent effect of the atmosphere. As no illumination correction was applied, the

topographically induced illumination variations are emphasised due to a reduction of the

atmospheric blurring effect. Thus the relief is pronounced. Moreover the spatial resolution

seems improved by a reduction of the atmospheric hazing. Details in the valley bottom as well

as in the alpine agricultural regions are enhanced as a result of the correction. All colours are

more saturated in comparison with the raw image, e.g. the blue of the lakes and the green of the

meadows. The image appears homogeneous over the various altitudes. No artefacts brought in

by the atmospheric correction can be detected. An impressive improvement of the satellite data

from a visual point of view could be obtained.

The correction of the illumination effects using the trigonometric approach of Kondratyev

[13] (Fig. 2c) is successful only to a certain degree. In the medium and highly illuminated

areas the illumination effect is corrected properly. The relief impression got lost and these parts

of the image appear flat, best seen in the little valley depicted in the zoom section. The faintly

illuminated surfaces, however, are overcorrected and expose artefacts, e.g. along the ridges

and in the left side of the zoom section. The correction of the illumination effects based on the

horizon line approach (Fig. 2d) proved to be the most successful. Most of the artefacts could

be eliminated, although along the ridges some overcorrected pixels remain. They are most

probably due to an insufficient spatial resolution of the DEM used in the study, because tests on

the exact location of the artefacted areas revealed an inadequacy in the data sets of cos(i) and of

the cast shadow.


The impact of the DEM inaccuracies is emphasised by the mixed signature problem.

Surfaces along ridges in "Beckenried" are often bare limestone with high reflectance properties.

A pixel of the region just 'behind' the ridge consists of dark shadowed areas and to some

extent illuminated and highly reflective limestone. The mixed signature of such a ridge pixel is

influenced by the brightening effect: with regard to the proportion of dark and bright parts

within the pixel, the surface appears to be bright, and as a consequence it is overcorrected.

B. Statistical analysis

Fig. 3 shows histograms of band 2 radiometrically raw (Fig. 3a) and corrected for atmo-

spheric (Fig. 3b) and illumination effects (Figs. 3c and 3d) in the Buochserhorn area, a

subsection within the test site "Beckenried" (Swiss Topographic Map coordinates

673'250 | 202'550 upper left and 677'660 | 199'250 lower right corner). The site was

chosen as it contains areas of predominantly forest and alpine agriculture in various illumination

conditions between 400 and 1800 m. In the spectral range of band 2 the correction of

illumination-effects should result in a bimodal histogram, the peaks representing forest and

alpine agricultural areas. In contrast to this, the histograms of the radiometric raw and the

atmospheric corrected image should appear non-bimodal, since they are influenced by the

impact of topographically induced illumination effects.

Indeed the non-bimodality can be seen in the radiometrically raw band 2 (Fig. 3a),

although the blurring influence of the atmosphere reduces the impact of illumination on the

histograms shape. The atmospheric correction reveals a contrast enhancement by reducing the

scattering effect of the atmosphere. Thus illumination effects are emphasised and cause a strong

heterogeneous appearance of the objects in the satellite imagery. In spite of the predominant

presence of two discriminant object classes the histogram of the atmospheric corrected image

appears non-bimodal (Fig. 3b). By the combination of illumination and atmospheric correction

using the trigonometric approach (Fig. 3c), however, the impact of illumination on the

appearance of the histogram can be eliminated successfully. The bimodality of the histogram

clearly shows the frequency-distribution of the two dominant object classes forest and agri-

culture. By considering the horizon lines, the bimodality can be impressively enhanced

(Fig. 3d).

Figs. 4a-c demonstrate that a correction of atmospheric and illumination effects does not

always lead to an enhancement of the bimodality in a frequency distribution of two object

classes. Fig. 4a shows the histogram of arable land, meadows and farm pastures combined

with alpine agricultural areas in test site "Beckenried" in the raw data of TM 5. A weak but still

obvious bimodality can be observed for both object classes. The correction of the atmospheric

effects leads to a smoothing of the histograms (Fig. 4b) and to an even better fit of the two

objects' histograms. The correction of illumination effects (Fig. 4c) finally results in a nearly

ideal Gaussian distribution. This proves that the two object classes reveal an identical spectral


behaviour in band 5, but are influenced by atmospheric and illumination effects. This can lead

to an improved separability of the classes in the raw data: arable land, meadows and farm

pasture located on a mean altitude of 686 m are stronger affected by atmospheric effects than

the alpine agricultural areas which are found on a mean altitude of 1486 m. Thus the impact of

the atmosphere on the spectral appearance improves the classification, but one discriminates

altitude dependent atmospheric effects rather than spectral varying surfaces. Also test site

specific influences of illumination effects can bias the classification results: an object

predominantly lying in shadowed areas is probably easier to classify before an illumination

correction takes place.

C. Classification analysis

The impact of the radiometric correction on an image classification is evaluated for

various objects. Based on the ground reference data, a classification of Swiss Land Use

Statistics aggregates and the forest stand classes coniferous, deciduous and mixed stands is

performed. For both classifications a maximum likelihood procedure with four bands is

applied. Clouds and cloud shadows are omitted. A Kolmogoroff-Smirnov test was applied to

each of the TM bands in order to test the prerequisite of a normality distribution. Except for

water, which is very easy to classify, all object classes fulfil the normality test. The use of an a-

priori value is neglected, and the threshold value is set to three standard deviations resulting in

classifying between 95% and 99% of the training area. The measures used to assess the

classification accuracy are the producer, user and overall accuracy. The producer accuracy is

defined as the total number of correctly classified pixels in a category divided by the total

number of pixels of that category in ground reference data. The total number of correct pixels in

a category divided by the total number of pixels that were classified in that category is called

user accuracy [16]. The overall accuracy is simply the number of correct pixels of all categories

divided by the total number of pixels in ground reference data. User and producer accuracies

are class specific and have to be referenced for each class under assessment. The overall

accuracy, as the name indicates, is a general measure for the classification in a test site.

In order to prevent from impacts due to the selection of training sets, the complete ground

reference data as given in Tab. 1 was used for training and verification. The results of the

classification are depicted in Fig. 5. The indices (a) to (d) correspond to: raw (a), atmo-

corrected (b), atmo-illu-corrected without horizon line (c), and atmo-illu-corrected data with

horizon line (d).

The interpretation of Fig. 5a is difficult as no clear tendency is obvious. The

classification accuracies of forests is almost the same in (a), (b), and (d), even though the

producer and user accuracies vary. In (c) the accuracy is clearly lower. The accuracies of the

settlement and urban areas perform best in (a), because the atmospheric correction in (b), (c),

and (d) seems to reduce the classification. Orchards, vineyards, and horticulture are not much


influenced by the radiometric correction steps and remain almost the same in (a) to (d).

Meadows, arable land and farm pastures are clearly improved, predominantly by the

atmospheric correction (b), but - as expected from the histogram analysis - the classification of

alpine agricultural areas is strongly weakened in (b), (c), and (d) since differences in

geoecological niche populations adapted to specific illumination conditions are smoothed out.

The classification of lakes and rivers, water shores, shore vegetation, wetlands, and other

unproductive areas remain almost untouched by the radiometric correction. Thus it must be

concluded that the radiometric correction has almost no effect on the classification accuracy of

all eight aggregates.

Fig. 5b illustrates the results of the forest stand classification in the Buochserhorn area,

showing the overall accuracy and the number of unclassified pixels. Here, the classification

accuracy can be enhanced considerably by the radiometric correction steps. The atmospheric

correction (b) reveals an improvement of 1 %, the illumination correction without horizon line

(c) more than 3 %, and the correction considering the horizon line (d) an improvement of

almost 7 %. In addition, the number of unclassified pixels rejected by the threshold is reduced

by the radiometric correction. Thus the spectral signatures became more distinctive after the

radiometric correction.

V. CONCLUSIONS

In this study a physically-based radiometric correction model is developed in order to

improve a land use classification. The methodology is non-empirical and therefore in principal

applicable to any test site, scene and sensor within a range of 0.25 and 4.0 µm. It is proved

that atmosphere and illumination variations have a crucial impact on the spectral appearance of

object classes in a satellite data set. Choosing training area samples and deciding on aggregating

object classes should therefore only be performed after a radiometric correction. Adjacent

slopes cause considerable additional irradiance in faintly illuminated areas. Thus an appropriate

calculation of the terrain irradiance is essential in rugged terrain. Also the isotropic diffuse

irradiance (sky irradiance) cannot be neglected in steep terrain. The simplified trigonometric

approach to calculate sky- and terrain-factors is insufficient in rugged terrain. It causes artefacts

and leads to misclassifications in the faintly illuminated areas. The more sophisticated method

to determine the local horizon line is computer time-intensive, but results in an impressive

improvement of the illumination correction in critical areas with a large amount of diffuse

irradiance.

The visual examination of the corrected images and statistical analysis clearly confirm the

effectiveness of the physically-based radiometric correction procedure. The three-dimensional

effect is enhanced in the atmospherically corrected image by improving the image contrast. The

image appears clear due to an impressive reduction of the atmospheric blurring effect. The

illumination correction reduces the relief impression and leads to a flat appearance of the image


particularly when the horizon line approach is used. Histogram analysis confirms the

elimination of the adverse effect of the atmosphere and topographically induced illumination

variations.

The assessment of the land use classification results is non-uniform. While the forest

stands discrimination could be improved markedly by the correction, other land use classes

were only slightly improved, remained unchanged or even worsened. This is in part due to

inadequacies in the ground reference data:

• the acquisition date of the ground reference data differs from the satellite overflight date:

the Swiss Land Use Statistics data set is 10 years older, and the forest stand maps are

about 5 years older

• the Swiss Land Use Statistics consists of sample points and does not contain surface

information

• the original categorisation of objects in the Swiss Land Use Statistics is mainly based on

land use and not on spectral homogeneity, e.g. class meadow consists of many different

kinds of species and even includes bare soil

• the forest canopy is not considered in the DEM data.

Furthermore the radiometric correction itself and the heterogeneity of the study area lead to a

test site specific decrease of the classification accuracy:

• as a result of atmospheric influence objects lying in low altitudes appear 'brighter' and

thus can be distinguished more easily from objects lying predominantly in higher alti-

tudes: the separation of meadows from alpine agricultural areas is easier before an

atmospheric correction is performed

• objects lying predominantly in steep slopes get a lower irradiance and appear 'darker' and

thus can be discriminated more easily from the surroundings before an illumination

correction takes place

• differences in geoecological niche populations adapted to specific illumination conditions

are smoothed out

• the heterogeneity of the surface land cover present in the study area can only be

inadequately registered by a 30 m pixel.

Unlike 'conventional' image enhancement techniques like histogram stretching or colour

look-up table manipulations, the physically-based radiometric correction takes vertical

variations of atmospheric effects into account. A still unsolved drawback in the physically-

based model, however, is the Lambertian assumption. The consideration of bidirectional effects

is beyond the scope of this study and will be treated within the Field-Goniometry and BRDF-


Research-Project at RSL [17]. It will help to further improve radiometric corrections and to

optimise land use classifications in rugged terrain.

VI. ACKNOWLEDGEMENT

This study was supported by the European Space Agency within ESA study no. 125487 and

by the Swiss National Science Foundation, grant no. 21-40539.94. The authors also wish to

thank M. Funk from VAW ETH, Zurich for the base of the horizon line determination

algorithm, and I. Leiss from RSL for help regarding classification procedures.


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pp. 764-770, 1993.

[4] St. Sandmeier, K.I. Itten, and P. Meyer, "Improvements of Satellite Land Cover

Classifications in Rugged Terrain Through Correction of Scene Related Effects," Final

Report ESA Study No. 125487, Dept. of Geography, University of Zurich, p. 51,

1994.

[5] P.N. Slater, S.F. Biggar, R.G. Holm, R.D. Jackson, Y. Mao, M.S. Moran, M. Palmer,

and B. Yuan, "Absolute radiometric calibration of the Thematic Mapper," SPIE,

vol. 660, pp. 2-8, 1986.

[6] B.L. Markham and J.L. Barker, "Spectral characterisation of the Landsat Thematic

Mapper sensors," Int. J. Remote Sensing, vol. 6, no. 5, pp. 697-716, 1985.

[7] C.R. Duguay and E.F. LeDrew, "Estimating Surface Reflectance and Albedo from

Landsat-5 Thematic Mapper over Rugged Terrain," Photogram. Eng. Remote Sensing,

vol. 58, no. 5, pp. 551-558, 1992.

[8] D.J. Gratton, P.J. Howarth, and D.J. Marceau, "Using Landsat-5 Thematic Mapper and

Digital Elevation Data to Determine the Net Radiation Field of a Mountain Glacier,"

Remote Sensing Envir., vol. 43, pp. 315-331, 1993.

[9] St. Sandmeier, "A Physically-Based Radiometric Correction Model - Correction of

Atmospheric and Illumination Effects in Optical Satellite Data of Rugged Terrain," Ph.D.

Thesis, Remote Sensing Series, Department of Geography, University of Zurich,

vol. 26, p. 140, 1995.

[10] C. Proy, D. Tanré, and P.Y. Deschamps, "Evaluation of Topographic Effects in

Remotely Sensed Data," Remote Sensing Envir., vol. 30, pp. 21-32, 1989.

[11] J.E. Hay, "Solar energy system design: the impact of mesoscale variations in solar

radiation," Atmos. Ocean, vol. 21, pp. 138-157, 1983.

[12] J. Dozier and D. Marks, "Snow mapping and classification from Landsat Thematic

Mapper data," Annals of Glaciology, vol. 9, pp. 97-103, 1987.

[13] K.Ya. Kondratyev, "Radiation in the Atmosphere," Academic Press, London, 1969.

[14] J. Dozier, J. Bruno, and P. Downey, "A faster solution to the horizon problem,"

Computers and Geosciences, vol. 7, pp. 145-151, 1981.

[15] J. Dozier and J. Frew, "Rapid Calculation of Terrain Parameters For Radiation Modeling

From Digital Elevation Data," IEEE Trans. Geosci. Remote Sensing, vol. 28, no. 5,

pp. 963-969, 1990.


[16] R.G. Congalton, "A Review of Assessing the Accuracy of Classifications of Remotely

Sensed Data," Remote Sensing Envir., vol. 37, pp. 35-46, 1991.

[17] St. Sandmeier, W. Sandmeier, K.I. Itten, M.E. Schaepman, and T.W. Kellenberger,

"The Swiss Field-Goniometer System (FIGOS)," in Proc. of IGARSS'95, Firenze, Italy,

pp. 2078-2080, 1995.


Tab. 1

ground reference class # of pixels

deciduous stands 31'443

mixed stands 35'922

coniferous stands 20'513

wooded areas 6'860

settlement and urban areas 741

orchards, vineyards, horticultures 254

arable land, meadows and farm pastures 3'202

alpine agricultural areas 1'731

lakes and rivers 5'354

water shores, shore vegetation, wetlands 25

other unproductive areas 500

An original of this Table is attached and should be used for publication


Fig. 1

45°

90°

135°225°

315°90°

0°

270°

180°

Vd45°

An original of Figure 1 is attached and should be used for publication


Fig. 2a-d

An original of the color plate is attached and should be used for publication


Fig. 3a

0

4000

8000

12000

16000

20000

12 17 22 27 32 37 42

Digital Numbers in TM 2

freq

uen

cy

An original of Figure 3a is attached and should be used for publication


Fig. 3b

0

1000

2000

3000

4000

5000

6000

0 10 20 30 40 50 60 70 80 90 100

rel. reflectance in TM 2

freq

uen

cy

An original of Figure 3b is attached and should be used for publication


Fig. 3c

0

1000

2000

3000

4000

5000

6000

0 10 20 30 40 50 60 70 80 90 100


freq

uen

cy

An original of Figure 3c is attached and should be used for publication


Fig. 3d

0

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0 10 20 30 40 50 60 70 80 90 100


freq

uen

cy



Fig. 4a

0

20

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10 20 30 40 50 60 70 80 90 100 110 120 130 140 150

Digital Numbers in raw TM 5

freq

uen

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arable land, meadowsand farm pastures

alpine agriculturalareas



Fig. 4b

0

20

40

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80

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30 40 50 60 70 80 90 100 110 120 130 140 150

rel. reflectance in atmo-corr. TM 5

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Fig. 4c

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rel. reflectance in atmo-illu-corr. TM 5

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Fig. 5a

0.0

10.0

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30.0

40.0

50.0

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100.0

a b c d a b c d a b c d a b c d a b c d a b c d a b c d a b c d

accu

racy

[%

]

user accuracy producer accuracy

woodedareas

settle-ment

orchardsetc.

mea-dows etc.

alpine agri-cultural areas

lakes andrivers

watershores etc.

other un-productive



Fig. 5b

42

44

46

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54

a b c d

ove

rall

accu

racy

[%

]

0

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un

classified p

ixels [%]

overall accuracy unclassified pixels



FIGURE AND TABLE CAPTIONS

Fig. 1: Local horizons for a point i. The inset depicts an azimuth projection of the horizon

angles of the same initial point i. The surface within the plot line corresponds to the

amount of sky seen from i. In the background the integrated sky-view factors Vd

can be seen in test site "Beckenried". Low values of Vd correspond to low bright-

ness and vice versa.

Fig. 2: TM imagery of 11 July 1991, bands 1 (blue), 2 (green) and 3 (red). On the right

side zoom sections (a') - (d') of the lower right corners in (a) - (d) are depicted.

(a) and (a'): Radiometrically uncorrected.

(b) and (b'): Atmospherically corrected.

(c) and (c'): Corrected for atmospheric and illumination effects using the

trigonometric approach to determine the sky- and terrain-view

factors.

(d) and (d'): Corrected for atmospheric and illumination effects using the

horizon line approach to determine the sky- and terrain-view

factors.

Fig. 3: Histogram of band 2 in the Buochserhorn area, a section of test site "Beckenried"

with predominantly forest and alpine agricultural areas.

top left (a): Radiometrically uncorrected.

bottom left (b): Atmospherically corrected.

top right (c): Corrected for atmospheric and illumination effects using the

trigonometric approach.

bottom right (d): Corrected for atmospheric and illumination effects using the

horizon line approach.

Fig. 4: Histogram of class arable land, meadows and farm pastures (in background) and of

class alpine agricultural areas (in foreground). Test site "Beckenried", TM band 5.

top (a): Radiometrically uncorrected.

center (b): Atmospherically corrected.

bottom (c): Corrected for atmospheric and illumination effects using the

horizon line approach to determine the sky- and terrain-view

factors.


Fig. 5: Maximum likelihood classification. The indices (a) to (d) in the Figures correspond

to: raw (a), atmo-corrected (b), atmo-illu-corrected without horizon line (c), and

atmo-illu-corrected data with horizon line (d).

top (a): User and producer accuracies of eight Swiss Land Use

Statistics aggregates in test site "Beckenried". The

classification is performed using bands 1, 3, 5, and 7.

bottom (b): Overall accuracy and unclassified pixels of forest stands

(deciduous, mixed, and coniferous) in the Buochserhorn area

of "Beckenried". The classification is performed using bands

2, 4, 5, and 7.

Tab. 1: Overview on ground reference data used in the classification. The forest stand

classes are derived from forest stand maps. The land use classes are based on

sample points provided by the Swiss Land Use Statistics.


Stefan Sandmeier received the M.Sc from University of Zurich

in 1991 and the Ph.D. in 1995. He is Research Scientist and

Project Manager at the Remote Sensing Laboratories. His major

interest are in the field of radiometric corrections and field-

goniometry / BRDF-research.

Klaus I. Itten received the M.Sc. and Ph.D. degrees in

geography from the University of Zurich, Switzerland, in 1969

and 1973, respectively.

In 1974 he joined NASA/GSFC for one year as a Research

Fellow of the European Space Agency. Since 1982 he was

Assistant Professor and acts since 1988 as Full Professor in

Geography at the University of Zurich-Irchel, where he is head of

the Remote Sensing Applications Division of the Remote Sensing

Laboratories. His research and teaching interests are remote

sensing and image processing for natural resources inventorying

and monitoring.

K.I. Itten is currently president of the Swiss Remote

Sensing Commission, member of the Swiss Commission on

Space Research, member of the Federal Commission of Space

Affairs, and acts as Swiss Delegate to the ESA Program Board

Earth Observation (PBEO).

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