ATCOR-4用户指南：大气与地形校正技术

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"ATCOR4手册V5.0" 《ATCOR4手册V5.0》是R.Richter为德国航空航天中心（DLR）遥感数据中心编写的关于机载影像大气与地形校正的用户指南，主要适用于版本5.0，发布于2009年1月。该手册的核心目标是提供一种方法来消除大气和地形对机载成像的影响，以获取更准确的地表反射率数据。 1. 大气与地形校正（ATCOR-4）： ATCOR-4是一种处理机载影像的技术，旨在校正由于大气条件（如大气吸收、散射）和地形因素（如斜坡和阴影）导致的数据失真。通过这种校正，可以得到地表真实的反射率信息，提高遥感图像的分析精度。 2. 基本概念 - 太阳辐射区域： - 辐射成分：讨论了太阳辐射的不同组成部分，包括直射辐射、散射辐射和地面反射辐射。 - BRDF校正：双向反射分布函数（BRDF）校正用于修正物体表面的非均匀光照效果，确保不同角度观测到的反射率一致。 - 光谱校准：确保不同波段之间的辐射测量一致性和精确性。 - 飞行中辐射校准：在飞行过程中进行的校准，以补偿传感器性能变化和环境影响。 - 去阴影：去除由地形或物体产生的阴影，提高图像的可见性和分析能力。 3. 基本概念 - 热辐射区域：在热红外区域，手册可能涉及大气窗口、热辐射传输以及如何校正大气对热辐射的影响。 4. 工作流程： - 程序概述：提供了整个校正过程的步骤，从输入原始数据到输出校正后的结果，包括数据预处理、大气模型选择、参数设置和结果验证等环节。 5. 示例：手册中展示了DLR研究所在Wessling的一个HyMap场景，用2004年6月7日记录的数据作为示例，解释了原始数字信号（DN）如何包含大气影响，并展示经过大气校正后获得的表面反射率光谱。通过这份手册，用户将能够理解和应用ATCOR-4算法，对机载遥感影像进行系统的大气和地形校正，从而提升地表特征分析的质量和准确性。这对于环境监测、土地覆盖分类、灾害评估等诸多遥感应用领域至关重要。

CHAPTER 2. BASIC CONCEPTS IN THE SOLAR REGION 16

is speci?ed. Other constituents vary slowly in time, e.g., the CO

concentration. ATCOR calcu-

lations were performed for a concentration of 380 ppmv. Ozone may also vary in space and time.

Since ozone usually has only a small in?uence ATCOR employs a ?xed value of 330 DU (Dobson

units, corresponding to the former unit 0.33 atm-cm, for a ground at sealevel) representing average

conditions. The three most important atmospheric parameters that vary in space and time are the

aerosol type, the visibility or optical thickness, and the water vapor. We will mainly work with

the term visibility (or meteorological range), because the radiative transfer calculations were per-

formed with the MODTRAN-4 code (Berk et al., 1998, 2000), and visibility is an input parameter

in MODTRAN. ATCOR employs a database of LUTs calculated with MODTRAN-4.

Aerosol type

The aerosol type includes the absorption and scattering properties of the particles, and the wave-

length dependence of the optical properties. ATCOR supports four basic aerosol types: rural,

urban, maritime, and desert. The aerosol type can be calculated from the image data provided

that the scene contains vegetated areas. Alternatively, the user can make a decision, usually based

on the geographic location. As an example, in areas close to the seathe maritime aerosol would be

a logical choice if the wind was coming from the sea. If the wind direction was toward the sea and

the air mass is of continental origin the rural, urban, or desert aerosol would make sense, depending

on the geographical location. If in doubt, the rural (continental) aerosol is generally a good choice.

The aerosol type also determines the wavelength behavior of the path radiance. Of course, nature

can produce any transitions or mixtures of these basic four types. However, ATCOR is able to

adapt the wavelength course of the path radiance to the current situation provided spectral bands

exist in the blue-to-red- region and the scenecontains reference areas of known re?ectance behavior.

The interested reader may read chapter 9.2.2 for details.

Visibility estimation

Two options are available in ATCOR:

? An interactive estimation in the SPECTRA module (compare chapter 5). The spectra of

di?erent targets in the scene can be displayed as a function of visibility. A comparison

with reference spectra from libraries determines the visibility. In addition, dark targets like

vegetation in the blue-to-red spectrum or water in the red-to-NIR can be used to estimate

the visibility.

? An automatic calculation of the visibility can be performed if the scenecontains dark reference

pixels. The interested reader is referred to chapter 9.2.2 for details.

Water vapor column

The water vapor content can be automatically computed if the sensor has spectral bands in water

vapor regions (e.g., 920-960 nm). The approach is based on the di?erential absorption method

and employs bands in absorption regions and window regions to measure the absorption depth,

see chapter 9.2.3. Otherwise, if a sensor does not possess spectral bands in water vapor regions,

e.g. Landsat TM or SPOT, an estimate of the water vapor column based on the season(summer

/ winter) is usually su?cient. Typical ranges of water vapor columns are (sea-level-to space):

tropical conditions: wv=3-5 cm (or g cm

- 2

)

midlatitude summer: wv= 2-3 cm

dry summer, spring, fall: wv=1-1.5 cm

dry desert or winter: wv=0.3-0.8 cm

CHAPTER 2. BASIC CONCEPTS IN THE SOLAR REGION 17

2.2 BRDF correction

The re?ectance of many surface covers depends on the viewing and solar illumination geometry.

This behavior is described by the bidirectional re?ectance distribution function (BRDF). It can

clearly be observed in scenes where the view and / or sun angles vary over a large angular range.

For ?at terrain scenes across-track brightness gradients that appear after atmospheric correction

are caused by BRDF e?ects, because the sensor ’ｓview angle varies over a large range. In extreme

caseswhen scanning in the solar principal plane, the brightness is particularly high in the hot spot

angular region where retrore?ection occurs, seeFigure 2.3, left image, left part. The opposite scan

angles (with respect to the central nadir region) show lower brightness values. A simple method,

called nadir normalization or across-track illumination correction, calculates the brightness as a

function of scan angle, and multiplies each pixel with the reciprocal function.

Figure 2.3: Nadir normalization of an image with hot-spot geometry.

Left: re?ectance image without BRDF correction. Right: after empirical BRDF correction.

The BRDF e?ect can be especially strong in rugged terrain with slopes facing the sun and others

oriented away from the sun. In areas with steep slopes the local solar zenith angle βmay vary

from 0

to 90

, representing geometries with maximum solar irradiance to zero direct irradiance,

i.e., shadow. The angle βis the angle between the surface normal of a DEM pixel and the solar

zenith angle of the scene. In mountainous terrain there is no simple method to eliminate BRDF

e?ects. The usual assumption of an isotropic (Lambertian) re?ectance behavior often causes an

overcorrection of faintly illuminated areas where local solar zenith angles βrange from 60

- 90

These areas appear very bright, seeFigure 2.4, left part.

To avoid a misclassi?cation of these bright areas the re?ectance values have to be reduced (Fig. 2.4,

center part). In ATCOR empirical geometry-dependent functions are used for this purpose. For

details the interested reader is referred to chapter 9.3.1, but the main idea is also discussed here. In

the simplest cases, the empirical BRDF correction employs only the local solar zenith angle βand

a threshold βT to reduce the overcorrected surface re?ectance ρL with a factor G, compare Figure

2.5.

ρcorrected = ρL G (2.10)

CHAPTER 2. BASIC CONCEPTS IN THE SOLAR REGION 19

a separate ?le ”*ilu ”．If the output ?le after atmospheric / topographic correction contains bright

overcorrected areas this ?le should be linked to the ”*ilu ”?le using any available standard image

processing software. The ”*ilu ”?le contains the illumination map β

scaled as byte data:

ilu = 100 ? cosβ

= arccos (ilu/ 100) (2.12)

(2.13)

Let us assume an example:

A pixel in a dark area of the ”*ilu ”image has the value ilu=32, i.e., β

= 71

. The overcorrected

re?ectance value be ρL = 80% and this value shall be reduced to 40%, a value typical for the ?at-

terrain neighborhood. Then the threshold angle has to be speci?ed such that cosβ

/cos β

= 0.5

(with exponent b=1 in equation (2.11), in this caseβT = 50

. So, if the desired re?ectance reduction

factor is G then the required threshold angle can be calculated from eq. (2.11 with b=1):

= arccos(

cosβ

) = arccos

ilu

100G

(2.14)

In many casesa separate treatment of BRDF e?ects for soil/rock and vegetation provides better

results. For this purpose, two modes of BRDF correction are available for vegetation, compare the

graphical user interface of Figure5.13. The ?rst mode is superior in most cases.

2.3 Spectral calibration

This section can be skipped if data processing is only performed for imagery of broad-band sensors.

Sensor calibration problems may pertain to spectral properties, i.e., the channel center positions

and / or bandwidths might have changed compared to laboratory measurements, or the radiometric

properties, i.e., the o?set ( co ) and slope ( c1 ) coe?cients, relating the digital number (DN) to the

at-sensor radiance L = c

+ c

? DN . Any spectral mis-calibration can usually only be detected

from narrow-band hyperspectral imagery as discussed in this section. For multispectral imagery,

spectral calibration problems are di?cult or impossible to detect, and an update is generally only

performed with respect to the radiometric calibration coe?cients, seechapter 2.4.

Surface re?ectance spectra retrieved from narrow-band hyperspectral imagery often contain spikes

and dips in spectral absorption regions of atmospheric gases (e.g., oxygen absorption around 760

nm, water vapor absorption around 940 nm). These e?ects are most likely caused by a spectral

mis-calibration. In this case, an appropiate shift of the center wavelengths of the channels will

remove the spikes. This is performed by an optimization procedure that minimizes the deviation

between the surface re?ectance spectrum and the corresponding smoothed spectrum. The merit

function to be minimized is

(δ) =

i =1

{ ρ

surf

(δ) - ρ

smooth

}

(2.15)

where ρ

surf

(δ) is the surface re?ectance in channel i calculated for a spectral shift δ, ρ

smooth

is the

smoothed (low pass ?ltered) re?ectance, and n is the number of bands in each spectrometer of a

hyperspectral instrument. So the spectral shift is calculated independently for each spectrometer.

In the currently implemented version, the channel bandwidth is not changed and the laboratory

values are assumed valid. More details of the method are described in [25]. A spectral re-calibration

CHAPTER 2. BASIC CONCEPTS IN THE SOLAR REGION 20

should precede any re-calibration of the radiometric calibration coe?cients.

Input to the spectral calibration module are the DN spectra of selected ?elds saved as ASCII ?les in

the SPECTRA module by pressing the button ”Savelast spectrum ”．The ?les should be numbered

consecutively, starting with a name such as ”location target1 ”，without extension. The next target

has to be named ”locationtarget2 ”etc. For each target ?eld three ?les will generated, for example:

? ”location target1.dat ”: contains surface re?ectance spectrum,

? ”location target1.txt ”: contains target coordinates, and processing parameters (visibility,

water vapor column, etc.),

? ”location target1 dn1.dat ”: contains the DN spectrum.

For a given location (scene) up to 9 targets can be extracted and used for the spectral calibration.

The geometry, scene visibility and average water vapor content of the targets enter as parameters to

the spectral calibration, see chapter 5.13 (Fig. 5.30). The water vapor content has to be averaged

from the values found in the ”location target*.txt ”?les. The ?rst target DN ?le has to be selected

by the user, the remaining target ?les are automatically found provided the nomenclature with a

consecutive numbering is applied. The result of the spectral calibration are ?les with the spectral

shifts per spectrometer and the new center wavelengths of all channels. The spectral bandwidth of

channels is not modi?ed.

Figure 2.6 shows a comparison of the results of the spectral re-calibration for a soil and a vegetation

target retrieved from an AVIRIS scene (16 Sept. 2000, Los Angeles area). The ?ight altitude was

20 km above sealevel (asl), heading west, ground elevation 0.1 km asl, the solar zenith and azimuth

angles were 41.2

and 135.8

. Only part of the spectrum is shown for a better visual comparison

of the results based on the original spectral calibration (thin line) and the new calibration (thick

line). The spectral shift values calculated for the 4 individual spectrometers of AVIRIS are 0.1,

-1.11, -0.88, and -0.21 nm, respectively.

Figure 2.6: Wavelength shifts for an AVIRIS scene.

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