Automatic DSM/DTM generation reproduces not only general features, but also detailed features of the terrain relief. Height accuracy of around 1 pixel in cooperative terrain. RMSE values of 1.3-1.5 m (1.0-2.0 pixels) for IKONOS and RMSE values of 2.9-4.6 m (0.5-1.0 pixels) for SPOT5 HRS. For 3D city modeling, the manual and semi-automatic feature extraction capability of SAT-PP provides a good basis. The tools of SAT-PP allowed the stereo-measurements of points on the roofs in order to generate a 3D city model with CCM The results show that building models with main roof structures can be successfully extracted by HRSI. As expected, with Quickbird more details are visible

Gruen, A.

Kocaman, S.

Zhang, L.

NASA Technical Reports Server

1HIGH ACCURACY 3D PROCESSING OF 
SATELLITE IMAGERY
A. Gruen, L. Zhang, S. Kocaman 
Institute of Geodesy and Photogrammetry, ETH Zurich, CH-8092 Zurich, Switzerland
http://www.photogrammetry.ethz.ch
JACIE Civil Commercial Imagery Evaluation Workshop, March 14-16, 2006, Laurel, Maryland, U.S.A.
https://ntrs.nasa.gov/search.jsp?R=20070038223 2019-08-29T18:37:22+00:00Z
2outline
• introduction
• sensor modeling of HRSI
• DTM/DSM generation from HRSI
• performance evaluation
• 3D city modeling with HRSI
• conclusions
3• High-resolution PAN & MS imagery
+ Quickbird (0.7 m)
+ IKONOS (1.0 m)
+ SPOT (2.5-10 m)
+ ALOS / PRISM (2.5 m) ……. 
• More than 8-bit images, higher dynamic range
• Along- / cross-track stereo;
Possibly multiple view terrain coverage
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
• Challenge:
+ Algorithmic redesign
+ Improvements
4Original IKONOS Image
Enhanced IKONOS Image
More than 8-bit images, higher dynamic range
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
5Acquisition Time: 2003-Dec-11
Acquisition Time: 2003-Dec-25
• Along- / cross-track stereo;
Possibly multiple view terrain coverage
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
6SAT-PP (Satellite Image Precision Processing) -- High-Res Satellite Imagery (HRSI): ≤ 5 m
+ New Processing Methods / Products for HRSI
+ Joint Sensor Model for IKONOS, QuickBird, SPOT, ALOS/RPISM and etc.
+ Specially Designed Image Matching Procedure for Linear Array Imagery
Input
Imagery with 
Supplementary 
Data
+ IKONOS
+ QuickBird
+ SPOT ……
GCPs
SAT-PP
+ Image Orientation
+ Image Matching
+ Feature Collection
+ 3D City Modeling
+ Image Pan-Sharpening
+ DTM & Ortho-Image 
Generation
…….
Products
+ DSM / DTM
+ Ortho-Images
+ Pan-Sharpened 
Images
+ 3D Vector Data
+ Digital Image Maps
+ 3D City Models
…….
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
7Functionality of SAT-PP
9 Project and data management tools, image format conversion and 
pre-processing, image display / roaming in mono and stereo modes
9 Sensor models (RFM, affine and projective DLT model) 
9 Orientation of single stereo models 
9 On-line quality control and error analysis via interaction of graphics 
elements 
9 GCP and tie point measurement in manual and semi-automated 
modes
9 Derivation of quasi-epipolar images for stereo mapping and 
feature collection
9 Automated DSMs generation
9 Generation of orthorectified images
9 Mono-plotting functions with DTMs
9 Manual and semi-automatic object extraction in mono/stereo
9 3D city modeling by using CyberCity ModelerTM
9 Pansharpening image generation. Fully automated sub-pixel 
image registration between multispectral and panchromatic imagery
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
8Workflow of SAT-PP
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
9Sensor Modeling and Blockadjustment
• Rigorous sensor model 
+ Physical imaging geometry (nearly parallel projection in along-track and perspective projection in cross-
track); high accuracy; easier for statistic analysis
- Mathematically more complicated; depends on type of sensors
• Sensor model based on RFM
+ Given (for IKONOS, Quickbird) and computed RFM parameters (RPCs)
• Blockadjustment model (Grodecki & Dial; 2003)
+ Calibrated system with a very narrow FOV; accurate a priori exterior orientation data  (HRSI -- OK !)
• Other simpler sensor models
+ 3D affine; relief-corrected 2D affine; DLT …...
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
10
For SPOT5 HRS / HRG
Fitting accuracy: 1/20 - 1/100 pixels
Tao & Hu; 2001
Sensor model based on RFM
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
11
User interface for block adjustment
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
12
Ellipse fitting mode
Line intersection mode
Ellipse fitting method GCP measurement
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13
Detailed DSM Generation
The approach uses a coarse-to-fine hierarchical solution with an effective 
combination of several image matching algorithms and automatic quality control.
The new characteristics provided by the IKONOS and Quickbird imaging 
systems, i.e. the multiple-view terrain coverage and the high quality image data, 
are also efficiently utilized.  
It was originally developed for multi-image processing of the very high-resolution 
TLS/StarImager aerial Linear Array images.  Now it has been extended and has 
the ability to process other linear array images as well.
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
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Workflow of Automated  DSM Generation
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
15
• Multiple image matching 
+ Matching guided from object space
+ Simultaneouslly multiple images (>= 2) with
Geometrically Constrained Cross-Correlation
• Matching with multiple primitives --- points + edges
• Self-tuning matching parameters
• High matching redundancy
• Efficient surface modeling
+ TIN (from a constrained Delauney triangulation method)
• Coarse-to-fine Hierarchical strategy
Automated DSM Generation Procedure
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
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Matching guided from object space
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
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Self-tuning matching parameters
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
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RPC + 2 Translates
GCPs(CPs)    RMSE-X  RMSE-Y RMSE-Z
0 (124)            2.75        2.00        1.97
1 (123)            0.48        0.35        0.90
4 (120)            0.49        0.36        0.86
124 (0)            0.45        0.33        0.81
IKONOS Triplet, 
Hobart, Australia
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
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RPC + 2 Translates
GCPs(CPs)    RMSE-X  RMSE-Y RMSE-Z
0 (124)            2.75        2.00        1.97
1 (123)            0.48        0.35        0.90
4 (120)            0.49        0.36        0.86
124 (0)            0.45        0.33        0.81
IKONOS Triplet, 
Hobart, Australia
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
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RPC + 2 Translates
GCPs(CPs)    RMSE-X  RMSE-Y RMSE-Z
0 (124)            2.75        2.00        1.97
1 (123)            0.48        0.35        0.90
4 (120)            0.49        0.36        0.86
124 (0)            0.45        0.33        0.81
IKONOS Triplet, 
Hobart, Australia
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
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RPC + 2 Translates
GCPs(CPs)    RMSE-X  RMSE-Y RMSE-Z
0 (124)            2.75        2.00        1.97
1 (123)            0.48        0.35        0.90
4 (120)            0.49        0.36        0.86
124 (0)            0.45        0.33        0.81
IKONOS Triplet, 
Hobart, Australia
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
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Automatic DTM/DSM Generation (IKONOS, Hobart, Australia)
Raster DSM (5 m Spacing)
+ The strip encompassed buildings and suburban housing 
in central and southern Hobart; accuracy 0.25 m
+ 111 GCPs as reference:
RMSE-Z: 0.9 m;    Mean: -0.3 m;   Absolute max.:  2.9 m
+ LIDAR DSM as reference (ca. 252000 points):
RMSE-Z: 2.7 m;    Mean: -0.2 m;   Absolute max.: 29.6 m
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
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RPC + 2 Translates
GCPs(CPs)     RMSE-X  RMSE-Y RMSE-Z
0 (43)            23.11      25.17       75.76
1 (42)              4.69        4.38         2.26
4 (39)              4.68        4.35         2.25
43 (0)              4.63        3.66         2.20
SPOT5-HRS, 
Bavaria, Germany
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
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RPC + 2 Translates
GCPs(CPs)     RMSE-X  RMSE-Y RMSE-Z
0 (43)            23.11      25.17       75.76
1 (42)              4.69        4.38         2.26
4 (39)              4.68        4.35         2.25
43 (0)              4.63        3.66         2.20
SPOT5-HRS, 
Bavaria, Germany
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
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RPC + 2 Translates
GCPs(CPs)     RMSE-X  RMSE-Y RMSE-Z
0 (43)            23.11      25.17       75.76
1 (42)              4.69        4.38         2.26
4 (39)              4.68        4.35         2.25
43 (0)              4.63        3.66         2.20
SPOT5-HRS, 
Bavaria, Germany
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
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RPC + 2 Translates
GCPs(CPs)     RMSE-X  RMSE-Y RMSE-Z
0 (43)            23.11      25.17       75.76
1 (42)              4.69        4.38         2.26
4 (39)              4.68        4.35         2.25
43 (0)              4.63        3.66         2.20
SPOT5-HRS, 
Bavaria, Germany
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
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Study area: Bavaria, Germany
+ Area: 120 × 60 Km2
+ Height range: ca. 1600 m
SPOT HRS stereo pair
+ Acquisition time: 1st October, 2002
+ 5m / 10m res. In along-/cross-track
Reference data:
+ 81 GPS GCPs (only 41 used)
+ 6 reference DTMs
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
Automatic DTM/DSM Generation (SPOT5-HRS, Bavaria, Germany)
28
Raster DSM (25 m Spacing,120 × 60 km2)
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
Automatic DTM/DSM Generation (SPOT5-HRS, Bavaria, Germany)
29
Reference DSM (5 m)                                             SPOT5 DSM (25 m)
Automatic DTM/DSM Generation (SPOT5-HRS, Bavaria, Germany)
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
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Reference DSM (25 m)                                            SPOT5 DSM (25 m)
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
Automatic DTM/DSM Generation (SPOT5-HRS, Bavaria, Germany)
31
DSM Accuracy (All Reference Data)
DSM Accuracy (Without Trees)
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
Automatic DTM/DSM Generation (SPOT5-HRS, Bavaria, Germany)
32
IKONOS (5 Images, 2 Strips), Town of Thun, SwitzerlandSub-pixel accuracy in planimetry; ca. pixel accuracy in height
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
IKONOS Images, Thun, Switzerland
33
IKONOS (5 Images, 2 Strips), Town of Thun, Switzerland
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
IKONOS Images, Thun, Switzerland
Decreasing number of GCPs doesn’t decreasing the accuracy significantly
34
IKONOS (5 Images, 2 Strips), Town of Thun, Switzerland
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
IKONOS Images, Thun, Switzerland
Even M_3DAFF could achieve similar results (for IKONOS imagery)
35
Study area: town of Thun, Switzerland
+ Area: 17 × 20 Km2
+ Height Range: 1600 m
IKONOS Geo Product
Reference
+ 2m spacing LIDAR DSM as reference
accuracy: 0.5 m (1σ) for open areas;
1.5 m for vegetation & build-up areas
+ 50 GPS GCPs (only 39 used)
Automatic DTM/DSM Generation (IKONOS, Thun, Switzerland)
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
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Raster DSM (5 m Spacing) Generated from IKONOS Images
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
Automatic DTM/DSM Generation (IKONOS, Thun, Switzerland)
37
LIDAR DSM (2 m)                           IKONOS DSM (5 m)
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
Automatic DTM/DSM Generation (IKONOS, Thun, Switzerland)
38
LIDAR DSM (2 m)                           IKONOS DSM (5 m)
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
Automatic DTM/DSM Generation (IKONOS, Thun, Switzerland)
39
LIDAR DSM (2 m)                           IKONOS DSM (5 m)
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
Automatic DTM/DSM Generation (IKONOS, Thun, Switzerland)
40
Z_diff = LIDAR_DSM_Z - Interpolation(IKONOS_DSM)
Z_diff = Matched_POINT_Z - Interpolation(LIDAR_DSM)
(dense LIDAR points --> Less surface modeling errors)
+ Point number: ca. 14,327,000
+ RMSE: 3.30 m
+ Mean: -0.32 m
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
Automatic DTM/DSM Generation (IKONOS, Thun, Switzerland)
41
Semi-automated Feature Extraction with SAT-PP
An extracted building from an IKONOS stereopair. The left building is 
measured manually and the right one is matched automatically.
- Currently available for some kind of objects, such as points, lines and polygons
- The user only needs to measure, for example, the outlines of buildings in one 
image.  The correspondences of building outlines in other images are computed 
automatically. 
- User intervention is possible for editing the polygon/line nodes when mismatching 
occurs
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
42
CyberCity Modeler approach, 
from stereo images and laser data
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
43
Point Cloud Coding System
• Independence regarding        
used DPS
• Reduction of manual labour
CyberCity-MODELERTM
Roof modeling
3D City Model
Visualization
(Formats: FLT, VT etc.)
CAD Systems
(Formats: DXF etc.)
Geometry 
improvement
Roof texturing
Digital 
Photogrammetric 
Station
Point cloud (x, y, z)
ArcGIS 9
(Shape, PGDB, ArcSDE)
+ DTM
Qu
al
ity
 C
on
tro
l
Facade texturing
Wall generation
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
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TEXTURING
Generic 
Texturing
• Texture library
• Not realistic
• Regional texture types
• Automatic
Automatic 
Texturing
• (Oblique-) Aerial Imagery
• Realistic
• Automatic
Terrestrial 
Texturing
• Digital Photographs
• Realistic / High resolution
• Manually applied
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
45
3D Object Extraction From IKONOS Imagery
Input & Data Pre-processing
Left stereo Right stereo
Date, time (local) 16/7/2000,
09:53
16/7/2000,
09:53
Sensor azimuth (°) 136.7 71.9
Sensor elevation (°) 61.4 60.7
Sun azimuth (°) 38.2 38.3
Sun elevation (°) 21.1 21.0
IKONOS Melbourne Stereopair
7x7 km area
elevation range of less than 100 m
32 GPS-surveyed ground measured semi-automatically by ellipse-fitting method
orientation was based on the supplied RPCs parameters (from Space Imaging) 
plus additional 6 affine transformation parameters in image space.
the RMSEs of orientation are 0.4 meters in planimetry and 0.9 meters in height.
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
46
3D Object Extraction From IKONOS Imagery
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
47
3D Object Extraction From IKONOS Imagery
Measurement area overview
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
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Generated 3D city model
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
3D Object Extraction From IKONOS Imagery
49
• Quickbird stereo images over Phoenix, USA
- Acquired on 9 April 2004
- Along track stereo images
- GSD: 75cm (mean)
- Viewing angles: 29º, - 27º
®DigitalGlobe
forward backward
3D City Modeling from Quickbird
• Detail in Quickbird images
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
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Facade textures from library
3D City Modeling from Quickbird
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
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3D City Modeling from Quickbird
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
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3D City Modeling from Quickbird
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
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Conclusions
SAT-PP: sophisticated image pre-processing algorithms, a set of sensor models, an 
image matching approach for DSM generation and feature extraction from HRSI.
Sensor modeling and block adjustment:
Basically three types of sensor-model orientation concepts at our disposal:
a) rigorous/physical sensor model
b) Rational Functional Model (RFM) with given RPCs
c) 2D affine model, possibly with added parameters
d) 3D affine and DLT models
Precise (sub-pixel) GCP / tie point collection (LSM) in semi-automatic model
Sub-pixel orientation accuracy can be achieved for all models
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
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Automatic DSM/DTM generation:
Reproduces not only general features, but also detailed features of the 
terrain relief
Height accuracy of around 1 pixel in cooperative terrain
RMSE values of 1.3-1.5 m (1.0-2.0 pixels) for IKONOS
RMSE values of 2.9-4.6 m (0.5-1.0 pixels) for SPOT5 HRS
3D city modeling:
Conclusions
The manual and semi-automatic feature extraction capability of SAT-PP 
provides a good basis also for 3D city modeling applications with 
CyberCity-ModelerTM (CCM).
The tools of SAT-PP allowed the stereo-measurements of points 
on the roofs in order to generate a 3D city model with CCM.  Additional 
features of CCM allow roof and facade texturing.
The results show that building models with main roof structures can be 
successfully extracted by HRSI.  As expected, with Quickbird more 
details are visible. 
INTRODUCTION SENSOR MODELING DSM GENERATION CASE STUDIES CONCLUSIONSCITY MODELING
55
Acknowledgements
¾ Prof. Fraser, Department of Geomatics, University of Melbourne
¾ Mr. Volpe, Eurimage S.p.A. 
¾ Dr. Poli and Dr. Wang, CyberCity AG
¾ Space Imaging, U.S.A.
¾ NPOC (National Point of Contact) - swisstopo, Bern


High Accuracy 3D Processing of Satellite Imagery

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