Quality Assessment, Atmospheric and Geometric Correction of airborne hyperspectral HyMap Data A. Brunna, C. Fischera, C. Dittmannb and R. Richterc a
Institute of Geotechnical Engineering and Mine Surveying, Technical University of Clausthal, Erzstrasse 18, D-38678 Clausthal Zellerfeld,
[email protected] b Deutsche Steinkohle AG, Division for Engineering Surveying & Geoinformation Services, c German Aerospace Centre, Remote Sensing Data Centre1
ABSTRACT This paper presents the results of the preprocessing steps performed on HyMap data which were recorded during the MINEO [1] flight campaign in summer 2000. The general objective of the project was the development of advanced analysis techniques to use earth observation data for monitoring tasks. Therefore it was necessary to perform several pre-processing steps which allow quantitative analyses of obtained reflectance data. These steps include quality control, geo-referencing and atmospheric correction of the imagery. The first step discusses the quality assessment of the data. For this purpose the calculation of the SNR with a semiautomatic detection of the brightest and most homogeneous area was done. The results allowed to separate channels with high noise from the initial dataset. The next step describes the geocorrection of the individual flight strips using the software package PARGE [2]. The aim was to generate a complete mosaic of the test area. A high definition DEM derived by photogrammetry and additional GPS control point measurements served as additional input data to optimize the correction accuracy. Further GPS measured ground control points were used as independent control points to estimate the transformation accuracy. The main step during pre-processing was the atmospheric correction using ATCOR 4 [2]. For this purpose ground reflectance measurements were employed. Additional weather data from a radiosonde was used to update the radiometric calibration of the HyMap data. The accuracy of the atmospheric correction was checked using ground reflectance measurements. Keywords: ATCOR 4, PARGE, SNR, preprocessing, atmospheric correction, geometric correction, hyperspectral imagery, HyMap
1 INTRODUCTION In August 2000 six mining sites all over Europe were recorded with the HyMap Sensor. This was done within the framework of the MINEO flight campaign. MINEO is a shared cost project funded by the European commission and the participants (IST-1999-10337). The aim of the project was to detect and to map environmental impacts caused by mining activities. In Germany Deutsche Steinkohle AG (DSK) together with its subcontractor, the Institute of Geotechnical Engineering and Mine Surveying (IGMC) of the Technical University of Clausthal was responsible for the “Central European” test site Kirchheller Heide. This test site is located at the northern border of the Ruhr Area in Northrhine-Westphalia, one of the biggest and most congested urban and industrial regions in Europe. The site is more than 100 km2 in size and has forests, nature reserves, meandering rivers and agricultural fields. It is used as a recreation area for the inhabitants of the cities in the neighborhood. On the other hand Deutsche Steinkohle AG is exploiting hard coal from more than 800 m below the surface and there are several open-cast mines where gravel and sand are exploited. This underground mining activity is the reason for surface subsidence and changes in the environmental balance (e.g. groundwater level, flowing directions, vegetation). The monitoring of the environmental balance and its changes is essential for this area. DSK is obliged by law to perform an environmental impact assessment. For this reason a Presented at the 3rd EARSeL Workshop on Imaging Spectroscopy, Herrsching, 13-16 May 2003
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large amount of environmental data like DEM, multispectral satellite imagery, aerial photographs and landuse mappings are available for this test site. Quality control and preprocessing of hyperspectral imagery is a prerequisite for an accurate evaluation of the data, especially if a change detection analysis should be performed or the data is used as a GIS Layer. A transfer of the radiance data into reflectance is necessary especially to compare the data with ground reflectance spectra, to carry out change detection analyses and to perform quantitative evaluations of vegetation spectra. That is why geo-referencing and especially atmospheric correction were performed. For that purposes we applied the methods of PARGE and ATCOR 4.
2 QUALITY ASSESSMENT AND SNR ESTIMATION Quality of hyperspectral datasets is highly dependent on several influences and parameters. The most important ones are noise content of the image dataset and the number of misalignments of image lines. A careful visual check of the delivered unprocessed data advised that there were no problems concerning misalignements in the image so that no longer care was taken on this point. Although the HyMap data is regarded as one of the best airborne hyperspectral data concerning noise content a visual check showed several highly noisy channels in the dataset. To ensure that all disturbing noisy channels can be separated from the image data a method was used which delivers comparable results for the estimation of the signal to noise ratio.
Figure1: comparison of an image part containing noise (left) and with little or no noise (right)
The signal is the part of the image without noise. All interfering signals are called noise. Noise is caused by several reasons, mainly from the scanning mechanism and the internal electronic and optical influences and can be of various types. Noise falsifies the signal significantly and therefore complicates information extraction. Each pixel p can be described by a robust statistical noise model, the additive noise model DN p , as the sum of the true image (a) and the noise (n) [3]:
DN p = int[a p + n p ]
(1)
Knowledge of the relative amount of the signal to noise ratio is necessary to assess noise reduction and information extraction methods. A device for the signal to noise ratio is SNR. It is dimensionless and consequently independent from the data dimension. The SNR is normally defined as a ratio between the signal and the noise of the image data [4]:
SNR =
C Signal C noise
With C representing the digital number of noise C noise and signal C signal and SNR the signal to noise ratio.
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(2)
A disadvantage of the SNR is that it can not be derived from data directly. To measure noise directly a specified target hast to be sensed and the parts of signal and noise have to be measured respectively. In most cases this is not possible and noise has to be estimated from the image data. The estimation for an image with random noise can be done by calculating the contrast ratio between a bright (highest local mean, LM) and a homogeneous (lowest local standard deviation, LSD) part of an image:
SNR =
LM LSD
(3)
To achieve less operator dependent results an operational method for estimating SNR has been developed which searches the brightest and most homogeneous image parts using image blocks with variable sizes [4]. For each of these image blocks the local mean (LM) is calculated as:
LM =
1 N
N
∑S i =1
i
(4)
S i is the signal value of the i th pixel in the block and N is the total number of pixels in the block. The block with the highest LM value is treated as the brightest block in the image. The local standard deviation (LSD) can be used as a measure for the homogeneity of an image block. Homogeneous blocks have small LSDs, while inhomogeneous blocks, such as those containing edges and texture features, have large LSDs. The local standard deviation can be calculated as: N 1 (S i − LM )2 LSD = ∑ ( N − 1) i =1
(5)
A favourable SNR is given if the signal predominates. This means that it can be clearly distinguished from the noise and its value is high. When signal and noise are less clearly distinguishable, the signal to noise ratio is claimed to be poor or low. This algorithm applied to the HyMap data showed that the SNR values are between 30 and nearly 600. The Figure 2 shows the SNR in the HyMap channels:
Figure 2: SNR of HyMap channels for the test site imagery
After all a number of 10 channels were separated from the image dataset because of a SNR below 200. These 10 channels all were located at transitions between two detector devices or were the first or last channels of the dataset respectively.
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3 PREPROCESSING Before thematic analysis of hyperspectral imagery can start pre-processing steps are required to transform this data into an appropriate format for further information extraction. This pre-processing stages consist primarily of geometric corrections, atmospheric corrections and mosaicking. Geometric pre-processing is necessary to integrate data into the GIS data base for further processing and analyses. This is done using the geo-correction algorithm PARGE. On the other hand it is required to make hyperspectral images from different aquiring dates spectrally comparable to each other, i.e. all adulterant effects of the atmosphere like haze, water vapor, various aerosols, which vary from date to date, have to be removed. This is done by the physical atmospheric correction model ATCOR 4 [5]. The geocoded and atmospheric corrected data then can be mosaicked. The established workflow for geo-coding and atmospheric correction is delineated in Figure 3. Input:
hyperspectral image (radiance data)
DEM
geocoding
geocoded hyperspectral
(PARGE)
image data
IMU data atmospherical data: GCP´s
geocoded & atmospheric
date of image, atmospheric
Acquisition, atmosphere profile
correction
(e.g. radiosonde
(ATCOR 4)
ascent),
corrected hyperspectral reflectance data
mosaicking
Visibility.
spectral reference measurements from the ground
Figure 3: Pre-processing workflow using PARGE & ATCOR 4
3.1
Geometric corrections
Airborne imaging in contrast to spaceborne image acquisition is characterized by a lower system stability in general. This results in geometric distortions due to instabilities in flight path, altitude, flight attitude and velocity of the airplane. These distortions can not be corrected accurately using traditional geo-referencing procedures, because the plane movements, given by roll, pitch, yaw and heading angles can not be described satisfactorily using polynomial transformations. A straightforward solution of the geometric correction of high resolution airborne data is the parametric approach used in PARGE. It recalculates the flight scan geometry pixelwise using auxiliary data. The three major types of auxiliary data are: image/scanner general information, navigation data and a digital terrain model (DEM). The accuracy of the geo-referencing procedure applying PARGE can reach sub pixel accuracy theoretically. The geo-referencing results normally have accuracies of up to 2 pixels [6]. But this quality is highly dependent on the given input data, especially the auxiliary navigation data and the accuracy of the flight path. Detailed information about the functionalities of the algorithm can be found e.g. at [5]. The geometric correction of the HyMap images was performed using PARGE version 1.2. Besides the image data the following auxiliary input data were required: - IMU data, - DTM, - Ground Control Points (GCP). The IMU data (inertial measurement unit) contains all necessary information about the sensor position during the flight, i.e. roll, pitch, heading and yaw angles, the GPS coordinates of the flight track, altitude and UTC time for every scanned image line. These data have been delivered by HyVista in a raw format (*.log file) and as a reformatted ASCII file (*.out file). The first step of the geo-referencing process was a transformation of the
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coordinate system, because all additional data relevant to this test site are available in the Gauß-Krueger-coordinate system. The input DTM for the geo-referencing procedure was a high resolution DTM derived from aerial photographs by digital photogrammetry and serves as a basic data set for the Environmental Impact Assessment (EIA) at the test site. To ensure a high geo-referencing accuracy, the DTM should be of higher resolution than the image data. The GIFOV of the HyMap images is 5 m, so a resampling of the elevation data to a pixelsize of 4 m has been stated as a suitable compromise between resolution and filesize. The geo-referencing procedure was performed separately for each image strip. For each strip GCP’s have been set. The amount of the GCP’s is individual for the strips, as it highly depends on the imaged surface and quality of flight path and navigation data. Coordinates of characteristic points such as road and way crosses have been used as GCP´s. To ensure good geo-referencing accuracy it was attempted to set the GCP´s well of nadir and spread them equally over the image. Although the algorithm used in PARGE theoretically needs only one GCP per strip to estimate the roll/pitch or x/y values to perform the geo-referencing, in practice one GCP per 100 image lines had to be used to ensure good accuracy. The coordinates for the GCP´s were measured by GPS or taken from a digital copy of the German Base Map (DGK5) with a scale of 1:5.000. The overall GCP accuracy is approx. ±1 m. The accuracy assessment of the geo-referencing procedure has been carried out computing the RMS error. The RMS error has been calculated using independent GCP’s. The calculated RMS error is 1,5 - 2 pixel varying between the strips. Transformed into meters, the RMS error does not exceed the displacement of 10 m. Figure 4 shows a part of the test site overlaid with a GIS Vector layer. It can be stated that as well the land use borders as the infrastructure like roads fit very well to the image data.
Figure 4: Example of geocoded HyMap image combined with GIS vector data sets
3.2
Atmospheric Correction
Varying atmospheric conditions, differences in sun geometry and topographic effects strongly influence the recorded signal. Different surface scattering components like adjacency radiation and terrain radiation are reflected and influence the recorded signal furthermore. These influences modify the actual spectral behavior of the recorded ground features. The objective of atmospheric correction is the elimination of atmospheric and illumination effects and the conversion of the data from at sensor radiance to reflectance. This is necessary to retrieve physical parameters from the surface, especially if vegetated. To carry out quantitative analysis and change detection applications with images from different periods of time and different sensors an accurate atmospheric correction is an essential part of pre-processing and a prerequisite for the derivation of products needed for subsequent image processing and analysis steps.
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Atmospheric correction can be done using two groups of approaches in general. The first group uses a purely statistical approach for the correction. These approaches calculate the parameters used for correction either from the image data directly (e.g. flat field calibration (FFC), dark-object-subtraction model (DOS)) or use ground reflectance spectra (e.g. the empirical line calibration (ELC)). These models ignore the true atmospheric conditions during image acquisition and some additional effects like illumination direction, topography of the represented surface or BRDF. The second group is based on physical approaches taking real atmospheric conditions into account. The Softwares use radiative transfer codes like MODTRAN. ATCOR 4, the model used here, uses such a physical model and is developed for wide field of view airborne scanners as an extension of the ATCOR 2 and ATCOR 3 algorithms. Additionally ATCOR 4 can use a digital terrain model to calculate adjacency scattering effects. The algorithm performs the atmospheric and topographic correction taking into account the angular dependence of the atmospheric correction functions [7].
Figure 5: Schematic sketch of radiation components: 1) path radiance; 2) reflected radiation from the viewed pixel; 3) adjacency radiation; 4) terrain radiation reflected to the pixel [7].
The choice of ATCOR 4 to accomplish the atmospheric correction for the data was driven by the following aspects: • • • •
3.2.1
the physical approach of ATCOR 4, using the MODTRAN4 radiative transfer code, enables to model the atmospheric conditions in a realistic way during the flight using current and ancillary atmospheric data and thus increase the accuracy of the procedure, enhanced handling capabilities of hyperspectral imagery, implemented characteristics of the HyMap sensor, IDL based with very good interfaces between ENVI and PARGE data formats.
Application of ATCOR 4
The atmospheric correction of HyMap data was done on geocoded and orthorectified image data. If a DEM is provided additionally the scan angles for each pixel can be considered as further assistance for the proper processing of rugged terrain. Because of the flat terrain of the test site this possibility was not applied in the actual works. The modular software package of ATCOR 4 delivers not only the ATCOR procedure but additionally a lot of additional tools e.g. to calculate an inflight calibration (IFCALI) and to create and resample atmospheric lookup tables (ATLUT and RESLUT). Figure 6 shows a brief flow chart of the correction steps.
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Figure 6: Flow chart of the processing procedure using ATCOR 4
The processing procedure of ATCOR 4 and the additional data needed for the processing can be summarized as follows. The generation of Atmospheric Lookup Tables (LUT´s) was performed with the ATLUT module of ATCOR 4. ATLUT calculates the LUT´s for the specified flight and solar geometry and the selected atmospheric parameters [7]. This aerological data can either be retrieved from standard atmospheric models integrated in the MODTRAN code or can be calculated based on additional data. The preferable solution is to use the height profile of a radiosonde ascent. In this case the radiosonde data from the weather station in Essen which is approx. 20 km. (air line distance) away from the test site were used. The sonde delivered data from the ground up to a height of approx. 24 km for the parameters temperature, dew point and water vapor pressure in steps of 2–4 km [8]. The visibility was measured on the ground and relative- ( f ) as well as absolute humidity (a ) were estimated using the equations 6 + 7 [9]: e f = E
a=
with:
(6)
0.793 ⋅ e T 1+ 273
e = partial vapor pressure, calculated as: e = 10
(7)
8.233⋅Td +184.2 234.67 + Td
E = saturation vapor pressure, calculated as: E =
Td = dew point
T = air temperature
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7.5⋅T 6.107 ⋅ 10 237 +T
(8) (9)
ATLUT submits all MODTRAN runs according the specified inputs. The output of ATLUT can be stored in a permanent spectral atmospheric database. Afterwards these spectral atmospheric databases have to be resampled using the band-specific response functions of the particular sensor using the module RESLUT. Based on the aerological data the LUT’s were calculated for the visibilities of 15, 25 and 50 km. The subsequent comparison of image retrieved reflectance spectra was done with the standard calibration file, provided by Integrated Spectronics Pty LTD... It showed good consistency in the spectral region below 1 µm for a visibility of 25 km. For each of the six HyMap strips the LUT for the visibility of 25 km was subsequently used. The atmospheric correction can be performed after these atmospheric LUT´s have been computed and the reference ground spectra were resampled to the sensors spectral properties. Prior to the correction a quality check of atmospheric input data and the radiometric calibration have been done. This quality check was performed using the IFCALI module within ATCOR 4, using the lookup-tables and the radiometric calibration mentioned above. Comparisons of image retrieved reflectance spectra using the standard calibration file with according ground reference spectra showed significant deviations up to 50% in the spectral regions beyond 1.5 µm necessitating a recalibration for the HyMap data. The IFCALI module has two modes of operation: the “reflectance” and the “calibration” mode. If the “reflectance” mode is activated, an evaluation of the atmospheric input data and the radiometric calibration can be performed. The “calibration” mode serves for the calculation of the calibration coefficients c0 and c1 for each reflective band that associates the measured digital numbers (DN) to the at-sensor radiance. Applying reference spectra a new calibration file can be computed. The inflight calibration was performed successively. First a calibration was done using the ground spectra from a homogeneous gravel pit recorded with a GER Mark V Spectrometer. This location was originally planned to serve as a calibration reference field. The two targets consist of white fine-grained sand and clay. Problems occurred because the spatial resolution of the DEM was not sufficient enough to follow the steep slopes of the terrain. Also, the homogeneity of these targets was not sufficient for the inflight calibration procedure. Using other vegetation spectra the best results have been achieved for the single target calibration at a corn field. Besides the comparison with the reference spectra, a well-known reflectance, e.g. water surface, has been extracted from the image to verify the results. The resulting reflectance matched best with the reference data in the range of 0.4-0.7 µm. However image retrieved water surface reflectance showed still too high values within the NIR spectral range. Therefore, a synthetic water reflectance spectrum with NIR reflectance values close to 1% was used for the improved recalibration. Applying the synthetic water reflectance and the reference reflectance of the mentioned corn field a new calibration file was generated which was used for the atmospheric correction of all six image strips.
Figure 7: Water surface reflectance (lake) retrieved after the single target calibration of the corn field (P-05) with its groundreflectance and the modified water surface reflectance (lake(mod), red) used for the inflight calibration; the vertical shaded bar marks the region with the best fit after single reference target calibration over the corn field.
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The ATCOR model is suitable to perform an accurate atmospheric correction using ground reflectance measurements as reference data. An essential part is the possibility to check spectra before processing the image cube and to calculate updated calibration files using reference spectra. The accuracy of the atmospheric correction method described here depends on the calibration accuracy of the sensor, the accuracy of the radiative transfer code, the quality of the digital terrain model and the image ortho-rectification.
Figure 8: Comparison of ground-reflectance spectra with the corresponding HyMap reflectance spectra for different fields; the shaded vertical bars mark regions of strong atmospheric water vapour and CO2 absorption.
CONCLUSION HyMap data have been atmospherically corrected and geo-referenced using ATCOR 4 and PARGE, additionally a quality control was made. For quality control a model was established, which makes it possible to derive channels with too much noise automatically. With this method for each image strips 10 spectral channels have been separated from the image dataset. These 10 channels were especially located at the transition between two spectrometer devices of the sensor. PARGE showed that it is possible to produce a proper geo-referenced image. A geo-referencing accuracy of approximately 2 pixels which denotes approx. 10 m. could have been achieved. Although theoretically subpixel accuracy is possible. The actual conditions prohibited a better accuracy. For this approach the theoretical number of 1 GCP per image strip was far from enough, though at least one GCP per 100 image lines was necessary to achieve this result. ATCOR 4 demonstrated its suitability to perform an accurate correction in combination with ground reference data. An essential part is the possibility to check spectra prior to the processing of the image cube and to calculate an updated calibration file using ground reference spectra. The accuracy of the atmospheric correction method described here depends on several factors: the calibration accuracy of the sensor, the accuracy of the atmospheric lookup tables, the quality of the DEM and the image ortho-rectification. The accuracy of the deviation of ground measured reflectance and retrieved reflectance is up to 3% in our case. The retrieved values are within error margins of ground measurements excepted for HyMap channels with noise problems and regions of strong water vapour absorption.
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ACKNOWLEDGEMENT The authors want to thank everybody who assisted the works. Especially thanks to BGR from the MINEO project cluster, Martin Schodlok from GFZ for his assistance of the field spectrometry campaign and Daniel Schläpfer from RSL for his interest and assistance during the geometric correction process.
REFERENCE [1] http://www.brgm.fr/mineo [2] http://www.rese.ch [3] ROTHFUSS, H., 1994: Verarbeitung und Einsatz Abbildender Spektrometerdaten (GER) mit unterstützenden Bodenmessungen zur Erkundung einer landwirtschaftlich genutzten Fläche. Fortschrittsbericht Reihe 15, Nr. 132. VDI, VDI-Verlag, Düsseldorf. [4] GAO, B.-C., 1993, An Operational Method for estimating signal to noise ratios from data acquired with imaging spectrometer, Remote Sensing of Environment, Vol. 43, No. 1, pp. 23-33. [5] RICHTER, R. AND SCHLÄPFER, D., 2000: A Unified Approach to Parametric Geo-referencing and Atmospheric / Topographic Correction for Wide FOV Airborne Imagery; Part 2: Atmospheric Correction. In: Proc.2nd EARSeL Workshop on Imaging Spectrometry, Enschede, 11 – 13 July, 2000. [6] SCHLAEPFER, D., 2001: PARametric GEocoding, User Guide V. 1.3.4.; ReSe Applications & RSL University of Zürich, Zürich. [7] RICHTER, R. , 2000: Atmospheric/Topographic Correction for Wide FOV Airborne Imagery: Model ATCOR 4 (Version 2.0 October 2000), report DLR-IB 564-04/00, Wessling. [8] BRUNN, A., DITTMANN, C., FISCHER, C., RICHTER, R., 2001 : Atmospheric Correction of 2000 HyMap Data in the Framework of the EU-Project Mineo, Proceedings of the SPIE Workshop, Toulouse. [9] HÄCKEL, H., 1990: Meteorologie, UTB, Stuttgart.
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