Remotely sensed data have geometric characteristics and representation which depend on the type of the acquisition system used. To correlate such data over large regions with other real world representation tools like conventional maps or Geographic Information System (GIS) for verification purposes, or for further treatment within different data sets, a coregistration has to be performed. In addition to the geometric characteristics of the sensor there are two other dominating factors which affect the geometry: the stability of the platform and the topography. There are two basic approaches for a geometric correction on a pixel-by-pixel basis: (1) A parametric approach using the location of the airplane and inertial navigation system data to simulate the observation geometry; and (2) a non-parametric approach using tie points or ground control points. It is well known that the non-parametric approach is not reliable enough for the unstable flight conditions of airborne systems, and is not satisfying in areas with significant topography, e.g. mountains and hills. The present work describes a parametric preprocessing procedure which corrects effects of flight line and attitude variation as well as topographic influences and is described in more detail by Meyer

Hansen, Earl G.

Itten, Klaus I.

Larson, Steven A.

Meyer, Peter

English

NASA Technical Reports Server

N_O-_ooru
Preprocessing: Geocoding of AVIRIS Data using Navigation,
Engineering, DEM, and Radar Tracking System Data
Peter Meyer*, Steven A. Larson, Earl G. Hansen, and Klaus I. Itten 1
Jet Propulsion Laboratory (JPL), California Institute of Technology
4800 Oak Grove Drive Pasadena, CA 91109, pmeyer@rsl.geogr.unizh.ch
1 University of Zurich, Remote Sensing Laboratories (RSL), CH-8057 Zurich
)
t"
1. INTRODUCTION
Remotely sensed data have geometric characteristics and representation
which depend on the type of the acquisition system used. To correlate such data over
large regions with other real world representation tools like conventional maps or
Geographic Information Systems (GIS) for verification purposes, or for further
treatment within different data sets, a coregistration has to be performed. In addition
to the geometric characteristics of the sensor there are two other dominating factors
which affect the geometry: the stability of the platform and the topography.There are
two basic approaches for a geometric correction on a pixel-by-pixel basis: (a) A
parametric approach using the location of the airplane and inertial navigation system
data to simulate the observation geometry and (b) a non-parametric approach using
tie points or ground control points (Itten and Meyer, 1993). It is well known that the
non-parametric approach is not reliable enough for the unstable flight conditions of
airborne systems, and is not satisfying in areas with significant topography, e.g.
mountains and hills. The present work describes a parametric preprocessing
procedure which corrects effects of flight line and attitude variation as well as
topographic influences and is described in more detail by Meyer (1993c).
2. BASIS OF THE STUDY
Test site and image data: The area "Zug-Buochserhorn" is the standard test site
of the Remote Sensing Laboratories, University of Zurich-Irchel in Central
Switzerland. The region was covered by the AVIRIS flight #910705, run 6 of the
NASA MAC Europe'91 campaign providing a data swath with an average nominal
pixel size of about 18m. The first scene, Zug, represents a hilly area with highest
elevation differences of about 600m and slopes with typical angles between 15 ° and
60 °. The second scene, Rigi, is an example of mountainous terrain with elevation
differences of about 1400m and maximum slope angles up to 90 °.
AVIRIS auxiliary data: The quality assessment for the actual data set is
described in detail in Meyer et al. (1993b). The current work uses the navigation data
roll, pitch, and true heading (generated through the ER-2 Inertial Navigation System)
and the roll and pitch of the AVIRIS instrument's precision gyros.
Digital elevation model (DEM): The test area is covered by the two digital
models (DHM-25) Zug and Rigi generated by the Swiss Federal Office of
Topography**. They have a resolution of 25m in i and j direction and of 0.10 m in
elevation e with an average error in elevation of 2.2m+1.0m for model Zug, and
4.4m_+l.8m for Rigi.
ADOUR conical radar tracking system: The ground-based immobile tracking
radar system ADOUR is a dual antenna, dual frequency radar with a conical scan
tracking system operated by the Swiss Air Force (Thomson-CSF, 1987). For the
*now at: University of Zurich-Irchel, Remote Sensing Laboratories, Winterthurerstrasse 190,
CH-8057 Zurich
**DEM data courtesy: Swiss Federal Office of Topography, July 05, 1993
127
https://ntrs.nasa.gov/search.jsp?R=19950017456 2020-06-16T08:30:03+00:00Z
currentapproach the three parameters latitude x, longitude y, and altitude z are
used. The systematic error for elevation and azimuth is + 0.2mrad and +7m for
distance with an update interval of 0.2 second.
Ground reference information: A forest map was generated by scanning the
green (forest) plate of the Swiss Topographic Map, scale 1:25,000, edition 1987 at
50 _tm with an Optronics 5040 Scanner. The average cartographic accuracy is about
5.0 m. A shoreline map was produced by digitizing the same map in an ARC/Info
using a digitizing tablet. The average (theoretical) accuracy is + 8.0m.
3. METHOD
Figure 1 gives an overview of the core task for the new method for geocoding
AVIRIS data. The basic goal is to reconstruct for every pixel the geometric situation at
the time it was acquired with AVIRIS. This includes three major aspects. The first
considers the flight line and attitude of the ER-2 aircraft, the second reconstructs the
current observation geometry and the third treats the situation on the surface. This
approach includes the three different coordinate systems (c,r) for the raw file,
longitude x, latitude y, and altitude z together with roll m, pitch q_,and true heading %
for the observation geometry and (i,j, ei,j) for the DEM.
Flight line and attitude of the ER-2 aircraft: The x,y, and z of the aircraft need to
be known as a first step. For the 1991 European flight the information results from an
unaided LTN90-116 INS navigation system with a position accuracy of 0.9 nmi/h
(Perrin, 1993). These data are not accurate enough for the current approach.
Therefore, AIX)UR data are used as an alternative. The description of the attitude of
the aircraft is based on the Z fr°m the navigation data and the 00and q) from the
instrument data.
Current observation geometry: The basic idea is shown in Figure 2 and
described in more detail by Larson et al. (1994). The effort is to obtain the underlying
surface out of the well-known location (=flight line) and the current attitude of the
aircraft. The position vector Xc, r represents the location of pixel (c,r) in the aircraft
coordinate system (x,y,z) at the instant the pixel was acquired by the instrument and
for the ideal case where m = q)= % = 0°:
xcr/{t [ caxol vllz/ (1)
where c = pixel number of pixel (c,r) within line r of the raw image, maxP =
maximum number of pixels per line (614), FOV = Field of View (in rad), and Zx, y =
altitude of ER-2 for the current x and y. The pixelwise calculation of the actual
pointing direction includes correction of the panoramic distortion. The vector Xc, r is
modified by rotations about the m, q), and % axis (vector X_'_r, Figure 2). The
transformed position vector Xc';r is computed as follows:
Xc_r = 1 - Xc, r •
o)
(2)
Situation on surface: The topography causes a shift in the apparent pixel
location, and affects the pixel size. The goal is now to find the intersection between
the pixel location vector X_'j and the surface of the DEM. Within the neighborhood
128
'" X.*.
around the transformed pixel location vector Xi, j, a test vector l,l is searched for,
which converges to X_','/.
X*,=
1,J
i - iNadi r
j - JNadir
(Zx,y-ei,j)
(3)
where iNadi r = 1-coordinate of the true nadir point, JNadir = j-coordinate of the true
nadir point, el, j = elevation of the test point at position (i,j), and Zx,y = altitude of
ER-2 for the current x and y. To allow for a more precise selection of the
corresponding surface point, the DEM oversampled to a grid size of 6m is used. To
define the intersection point on the surface, the normalized dot product of X* • andl,j
X* . best representing z,] is that for which the dot,pr X _tt.Xi, j is calculated. The vector l,j
product DP has the smallest difference from 1. To represent the pixel size dependent
on the topography, the four corner points (6m grid size) of every pixel are separately
calculated. To prevent changes to the radiometric characteristics, the original value is
selected by an improved extraction algorithm during the resampling to an 18m grid
size, thereby eliminating the need to interpolate the values.
4. DISCUSSION AND OUTLOOK
There are currently no well-established methods of quantitatively assessing
the success of a geocoding process. Visual inspection provides useful information,
but cannot be used to intercom,are methods. Statistical results based on residual
calculation of single ground control points allow only a local error assessment.
For the discussion, the Rigi scene is selected because of the more challenging
topography. Figure 3 shows bands 13, 18, and 28 of the geocoded image overlaid by
the scanned forested areas (green line) and digitized shoreline (blue line). The
enlarged areas are selected dependent on their aspect and slope angle. In general, the
results show a good correspondence between the geocoded image and the map for
all existing topographical locations. A few locations show minor miscorrespondence.
These problems are almost always restricted to single pixels and no general tendency
can be recognized. The errors may result from changes in reality between the time
the aerial photographs (which are the basis for the topographic maps) were acquired
(1987) and the AVIRIS data acquistion, and on the fact that maps are the result of a
generalization while AVIRIS displays every occurrence within its resolution
characteristics. Figure 3B demonstrates another problem of the ground reference
information. While the lower regions of the forested slope correspond perfectly, the
AVIRIS image extends significantly the forest area on the upper limits. This
"misregistration" is based on the problem of the determination of the forest border
along the timber line and its representation in the forest map through symbolic point
signatures which are suppressed through the scanning process. Figure 3D and Figure
3E portray areas with rapidly changing slope angles. The blue shoreline proves for
subarea (D) and the green forest line for subarea (E) the good correspondence. The
additional verification calculates the average deviation of the geocoded image
compared with the forest and lake map for checkpoints. Lines 1 and 2 of Table 1
indicate the results of this test and confirm the visual validation. No systematic
deviation was found. To double-check the performance of the new parametric
129
approach,sceneRigiwasgeocoded using the improved, non-parametric rubber-sheet
approach (Itten and Meyer, 1993). Table 2 (line 3) presents the result for the improved
rubber-sheet approach and shows the better performance of the parametric solution.
The datasets Zug and especially Rigi need georadiometric corrections.
Atmospheric corrections using radiative transfer models like MODTRAN-2a (Green
et al., 1993) as well as a compensation for the slope-aspect-dependent illumination
difference (Meyer et al.,1993a) should complete the preprocessing of the sensor,
system and scene related effects of the current data set.
The whole procedure was implemented using the IDL (Interactive Data
Language, a proprietary programming language, Research System Inc., 1993).
5. ACKNOWLEDGMENTS
The first author would like to thank the Swiss Research Foundation and
NASA for supporting the project and K.I. Itten (RSL), G. Vane, R. O. Green, and
E. G. Hansen (JPL) for providing special assistance. The support of the NASA Ames
Research Center, the Swiss Air Force, and Litton, Aero Products, Woodlands, CA is
gratefully acknowledged. Thanks for technical assistance go to R.O. Green,
T. G. Chrien, A. T. Murray, H. I. Novack, M. Solis, P. J. Nielsen, J. J. Genofsky, and
C. Chovit UPL) as well as to T. Kellenberger, M. Schaepman, S. Sandmeier, C. Ehrler,
E. H. Meier, S. Veraguth, U. Kurer, and D. Schlaepfer (RSL).
The research described in this paper was carried out at the Jet Propulsion
Laboratory, California Institute of Technology, and was sponsored by the Swiss
Research Foundation, Project No. 8220-33290.
6. REFERENCES
Green, R. O., J. E. Conel,T. G. Chrien (1993), Correction of AVIRIS Radiance to
Reflectance using MODTRAN-2, in Proc. International Society for Optical
Engineering (SPIE) 1937, Orlando, FL (in press).
Itten, K. I., and P. Meyer (1993), Georadiometric correction of TM-Data of
mountainous forest areas, IEEE Transactions on Geoscience and Remote Sensing
(in press).
Larson, S., P. Meyer, and E. G. Hansen (1994), Topographical and Geometric
Correction of Remote Sensing Image Data using Dead Reckoning, IEEE
Transactions on Geoscience and Remote Sensing (in preparation).
Meyer, P., K. I. Itten, T. Kellenberger, R. Leu, and S. Sandmeier (1993a), Radiometric
and Geometric Correction of TM-Data of Mountainous Forest Areas, ISPRS
Journal of Photogrammetry and Remote Sensing: 48 (in press).
Meyer, P., R. O. Green, and T. G. Chrien (1993b), Extraction of Auxiliary Data from
AVIRIS Distribution Tape for Spectral, Radiometric, and Geometric Quality
Assessment, in Proc. of the Fourth Annual JPL Airborne Geoscience Workshop,
Washington, D.C. Published by Jet Propulsion Laboratory, Pasadena,
California (this volume).
Meyer, P. (1993c), A Parametric Approach for the Geocoding of Airborne
Visible/Infrared Imaging Spectrometer (AVIRIS) Data in Rugged Terrain,
Remote Sensing of Environment (submitted).
Perrin, D. (1993), Personal communication (fax Jan 28, 1993).
Research Systems, Inc. ®. (1993), IDL User's Guide, Version 3.0, Boulder, CO.
Thomson-CSF (1987), Radar ADOUR, Modernisation t616m6trie et codage, Tome I.
130
Flight line and attitude
of the ER-2 aircraft
Determination of x,y, and z
with ADOUR data
Determination of the attitude with
navigation yaw, and
engineering pitch and roll
Current observation geometry
Determination of the ideal
pixel k',cation vector
Xc,r
Calculation of the transformed
pixel location vector
Xc,' D Xi',' i'
I
Situation on surface
Determination of the lest vector
for the pixel (c,r) with c=l
x_]j
Determination of the test vector
for the pixels (c,r) with c>l
x[j
Calculation of the
normalized dot product
Xi: j XI',' j
Ix;,jl lx l;I
Determination of the best
test point (BTP)
Transformation from
6m to 18m grid size
Selection of the most
frequently involved 6m pixel
I
Accuracy assessment
Determination of the
correspondence between the
geocod_xt image and the
forest and shoreline mask
ADOUR data ]I
Interpolalinn to [
[ 6m grid size [
AVIRIS auxiliary data
Correction for earth's
rotational effect (roll, pitch)
Determination of the offsets
(roll, pitch)
Correction for the declination
(true heading)
Interpolation to 6m grid size
r 1
I Digital Elevation Model I
[ Rt_sampling to 6m and 18m [
I grid size I
t -J
F--- I
! AVIRIS Image I
I data I
[ Ground Reference
[ Information
I
[ Scanning of the forest map
[ Digitizing of the shoreline
I Resampling to lSm grid size
t
[ Core tasks of thegeocoding
approach
F I
I I Data preparation
Figure 1: Overview of the data preparation and core tasks of the geocoding approach,
where x=latitude, y=longitude, z=altitude of the airplane, and c=column and r=row
of the raw file.
131
-Z -Z'
>/ x
I1..-;.-"
----.. l/." ..
"---. > LJ
f
-'4---- Ideal pixel
location vector Xc,r
with _=0, ¢0=0,X=0
Transformed pixel
location vector Xc'r
Figure 2: Principal outline of the observation geometry to calculate the transformed
pixel location vector where (_=pitch, re=roll, and/{=true heading, and x,y,z defining
the coordinate axis for the ideal pixel location vector and x',y',z' for the transformed
pixel location vector.
Figure 3 (Slide 7): (A) shows the composite rendered on the digital elevation model
with tags (B-E) for the location of the enlarged subareas of the horizontal (non-
rendered) composite. (B)-(E) show zoom-up (= factor 5) parts with different aspect
and slope angles.
Table 1: Result of the quantitative effort for the comparison between the geocoded images and the
forest map with the RMS for the east-west direction (i) and north-south direction (j) of the digital
elevation model. The check points used for the non-parametric approach for scene Rigi are a
selection from the entire number of points used for the parametric approach.
No. of checkpoints RMS for i-direction RMS for j-direction
Scene Zug (parametric approach) 186 0.07 pixel 0.19 pixel
Scene Rigi (parametric approach) 309 0.12 pixel 0.09 pixel
Scene Rigi (non-parametric approach) 249 3.57 pixel 1.37 pixel
132


Preprocessing: Geocoding of AVIRIS data using navigation, engineering, DEM, and radar tracking system data

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