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Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

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CC BY: Attribution 4.0 International. You can find more information here.

Bibliographic data

fullscreen: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

Monograph

Persistent identifier:
856473650
Author:
Baltsavias, Emmanuel P.
Title:
Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects
Sub title:
Joint ISPRS/EARSeL Workshop ; 3 - 4 June 1999, Valladolid, Spain
Scope:
III, 209 Seiten
Year of publication:
1999
Place of publication:
Coventry
Publisher of the original:
RICS Books
Identifier (digital):
856473650
Illustration:
Illustrationen, Diagramme, Karten
Language:
English
Usage licence:
Attribution 4.0 International (CC BY 4.0)
Publisher of the digital copy:
Technische Informationsbibliothek Hannover
Place of publication of the digital copy:
Hannover
Year of publication of the original:
2016
Document type:
Monograph
Collection:
Earth sciences

Chapter

Title:
TECHNICAL SESSION 4 FUSION OF SENSOR-DERIVED PRODUCTS
Document type:
Monograph
Structure type:
Chapter

Chapter

Title:
STRATEGIES AND METHODS FOR THE FUSION OF DIGITAL ELEVATION MODELS FROM OPTICAL AND SAR DATA. M. Honikel
Document type:
Monograph
Structure type:
Chapter

Contents

Table of contents

  • Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects
  • Cover
  • ColorChart
  • Title page
  • CONTENTS
  • PREFACE
  • TECHNICAL SESSION 1 OVERVIEW OF IMAGE / DATA / INFORMATION FUSION AND INTEGRATION
  • DEFINITIONS AND TERMS OF REFERENCE IN DATA FUSION. L. Wald
  • TOOLS AND METHODS FOR FUSION OF IMAGES OF DIFFERENT SPATIAL RESOLUTION. C. Pohl
  • INTEGRATION OF IMAGE ANALYSIS AND GIS. Emmanuel Baltsavias, Michael Hahn,
  • TECHNICAL SESSION 2 PREREQUISITES FOR FUSION / INTEGRATION: IMAGE TO IMAGE / MAP REGISTRATION
  • GEOCODING AND COREGISTRATION OF MULTISENSOR AND MULTITEMPORAL REMOTE SENSING IMAGES. Hannes Raggam, Mathias Schardt and Heinz Gallaun
  • GEORIS : A TOOL TO OVERLAY PRECISELY DIGITAL IMAGERY. Ph.Garnesson, D.Bruckert
  • AUTOMATED PROCEDURES FOR MULTISENSOR REGISTRATION AND ORTHORECTIFICATION OF SATELLITE IMAGES. Ian Dowman and Paul Dare
  • TECHNICAL SESSION 3 OBJECT AND IMAGE CLASSIFICATION
  • LANDCOVER MAPPING BY INTERRELATED SEGMENTATION AND CLASSIFICATION OF SATELLITE IMAGES. W. Schneider, J. Steinwendner
  • INCLUSION OF MULTISPECTRAL DATA INTO OBJECT RECOGNITION. Bea Csathó , Toni Schenk, Dong-Cheon Lee and Sagi Filin
  • SCALE CHARACTERISTICS OF LOCAL AUTOCOVARIANCES FOR TEXTURE SEGMENTATION. Annett Faber, Wolfgang Förstner
  • BAYESIAN METHODS: APPLICATIONS IN INFORMATION AGGREGATION AND IMAGE DATA MINING. Mihai Datcu and Klaus Seidel
  • TECHNICAL SESSION 4 FUSION OF SENSOR-DERIVED PRODUCTS
  • AUTOMATIC CLASSIFICATION OF URBAN ENVIRONMENTS FOR DATABASE REVISION USING LIDAR AND COLOR AERIAL IMAGERY. N. Haala, V. Walter
  • STRATEGIES AND METHODS FOR THE FUSION OF DIGITAL ELEVATION MODELS FROM OPTICAL AND SAR DATA. M. Honikel
  • INTEGRATION OF DTMS USING WAVELETS. M. Hahn, F. Samadzadegan
  • ANISOTROPY INFORMATION FROM MOMS-02/PRIRODA STEREO DATASETS - AN ADDITIONAL PHYSICAL PARAMETER FOR LAND SURFACE CHARACTERISATION. Th. Schneider, I. Manakos, Peter Reinartz, R. Müller
  • TECHNICAL SESSION 5 FUSION OF VARIABLE SPATIAL / SPECTRAL RESOLUTION IMAGES
  • ADAPTIVE FUSION OF MULTISOURCE RASTER DATA APPLYING FILTER TECHNIQUES. K. Steinnocher
  • FUSION OF 18 m MOMS-2P AND 30 m LANDS AT TM MULTISPECTRAL DATA BY THE GENERALIZED LAPLACIAN PYRAMID. Bruno Aiazzi, Luciano Alparone, Stefano Baronti, Ivan Pippi
  • OPERATIONAL APPLICATIONS OF MULTI-SENSOR IMAGE FUSION. C. Pohl, H. Touron
  • TECHNICAL SESSION 6 INTEGRATION OF IMAGE ANALYSIS AND GIS
  • KNOWLEDGE BASED INTERPRETATION OF MULTISENSOR AND MULTITEMPORAL REMOTE SENSING IMAGES. Stefan Growe
  • AUTOMATIC RECONSTRUCTION OF ROOFS FROM MAPS AND ELEVATION DATA. U. Stilla, K. Jurkiewicz
  • INVESTIGATION OF SYNERGY EFFECTS BETWEEN SATELLITE IMAGERY AND DIGITAL TOPOGRAPHIC DATABASES BY USING INTEGRATED KNOWLEDGE PROCESSING. Dietmar Kunz
  • INTERACTIVE SESSION 1 IMAGE CLASSIFICATION
  • AN AUTOMATED APPROACH FOR TRAINING DATA SELECTION WITHIN AN INTEGRATED GIS AND REMOTE SENSING ENVIRONMENT FOR MONITORING TEMPORAL CHANGES. Ulrich Rhein
  • CLASSIFICATION OF SETTLEMENT STRUCTURES USING MORPHOLOGICAL AND SPECTRAL FEATURES IN FUSED HIGH RESOLUTION SATELLITE IMAGES (IRS-1C). Maik Netzband, Gotthard Meinel, Regin Lippold
  • ASSESSMENT OF NOISE VARIANCE AND INFORMATION CONTENT OF MULTI-/HYPER-SPECTRAL IMAGERY. Bruno Aiazzi, Luciano Alparone, Alessandro Barducci, Stefano Baronti, Ivan Pippi
  • COMBINING SPECTRAL AND TEXTURAL FEATURES FOR MULTISPECTRAL IMAGE CLASSIFICATION WITH ARTIFICIAL NEURAL NETWORKS. H. He , C. Collet
  • TECHNICAL SESSION 7 APPLICATIONS IN FORESTRY
  • SENSOR FUSED IMAGES FOR VISUAL INTERPRETATION OF FOREST STAND BORDERS. R. Fritz, I. Freeh, B. Koch, Chr. Ueffing
  • A LOCAL CORRELATION APPROACH FOR THE FUSION OF REMOTE SENSING DATA WITH DIFFERENT SPATIAL RESOLUTIONS IN FORESTRY APPLICATIONS. J. Hill, C. Diemer, O. Stöver, Th. Udelhoven
  • OBJECT-BASED CLASSIFICATION AND APPLICATIONS IN THE ALPINE FOREST ENVIRONMENT. R. de Kok, T. Schneider, U. Ammer
  • Author Index
  • Keyword Index
  • Cover

Full text

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 
84 
errors). Height measurement fails for various reasons all over 
the DEM (e.g. temporal (radiometric or geometric) changes or 
sensor noise), therefore all resulting random errors will be 
summarized in a statistic error. The definition of blunders is 
adopted from the common multi-measurement case (e.g. wrong 
determination of ground control points). Note that blunders and 
systematic errors have (statistically) the same effect, as they 
cause an offset in the expectation values of the observations. 
3. CORRELATION COEFFICIENT 
The correlation coefficient R is defined as 
[x\xi] 
a/ £ 1 
[xix\] 
\e\ 
[X2X2) 
(-1 <p< 1) (1) 
Several authors give surveys of InSAR and stereo-optical DEM 
errors. Grim (1986), who refers to the Baarda error model, gives 
a detailed error description for photogrammetric sensor 
modelling. Zebker et al. (1994) gave an evaluation of ERS 
topographic map accuracy, dividing errors according to their 
effects in statistic and systematic errors. 
Each error type will occur in both InSAR and stereo-optical 
DEMs. In contrast to systematic errors and blunders, statistic 
errors of phase and parallax measurements occur locally, but 
may turn affected regions unusable. Typical reasons for statistic 
errors in spacebome DEM generation are temporal changes 
between both passes. As the InSAR procedure is very sensitive 
to a change of backscatter geometry, the errors in InSAR DEMs 
increase with the repeat pass time interval. Additional problems 
with the handling of mountainous terrain lead, in comparison to 
optical sensors, to a generally less robust performance. 
The presentation of erroneous heights is different in both 
DEMs. The stereo-optical DEM is affected by spikes, which 
make the DEM appear rugged. These spikes originate from the 
mismatching of conjugate points in the stereo pair. The 
resulting parallax causes a wrong height estimation, i.e. the 
point lies above or below the terrain (Fig. 4). The localization of 
these errors seems to be much easier than the error detection in 
the InSAR DEM. Although residues in the interferogram appear 
as phase jumps higher than 7t, these values are either not used or 
replaced by surrounding phase estimates, depending on the 
phase unwrapping algorithm. For this study, the least squares 
(LS) unwrapping algorithm (Ghiglia and Romero, 1994) has 
been applied, which estimates heights by integrating the phase 
vector gradient field under a smooth solution constraint, thus 
suppressing residuals and yielding a global solution. Although 
this algorithm performs robustly in comparison to the tree 
algorithm (Goldstein et al., 1988), errors are still localized in 
areas of low correlation in the LS DEM (Zebker and Lu, 1998). 
As stated above, interferometric and stereo DEM generation 
requires the correct identification of the same point in both 
images, in order to measure the phase difference or the parallax. 
Unlike systematic errors, which can be reduced by suitable 
choice of parameters, the reduction of statistic errors is the 
challenging act for the fusion process. The next section shows 
the meaning of the correlation coefficient for both techniques. It 
will be used as an indicator of the presence and extent of noise 
and consequently of the statistic error of each measurement. 
where E{Xi} denotes the expectation value of the statistic 
variable Xi, hence E{(Xi) 2 } is the variance and E{X1X2} the 
covariance of XI and X2. Note that coherence denotes the 
absolute value of p in interferometry. 
The correlation coefficient between two images is used in both 
techniques, but has for each technique a different meaning. In 
the stereo-optical case, points are matched by maximising the 
correlation coefficient within the correlation window. 
Therefore, the correlation coefficient of each DEM point shows 
the maximum correlation value occurring during the matching 
process. Therefore, low correlation indicates not necessarily a 
point error, but points, where matching problems may have 
occurred. 
Low correlation of grey values results from radiometric 
differences between the images, due to multitemporal and 
across track data acquisition (case of SPOT stereo viewing), 
sensor noise or atmospheric effects due to scattering and 
absorption, etc. Therefore, a low correlation coefficient will 
occur randomly all over a DEM, wherever differences between 
the stereo images exist (Fig. 3). Image pre-processing, like 
filtering and introduction of geometric and radiometric 
constraints are highly needed in the multitemporal case in order 
to reduce the effects of radiometric differences on the matching 
algorithm (Baltsavias and Stallmann, 1993). 
In the InSAR case, a high coherence indicates those areas, 
where phase estimation for DEM generation is reasonable. Even 
more, the cross-correlation would give an estimate of the height 
error, if it was known precisely, as 
a = K 
(2) 
with 
P 
1-/7 
(3) 
a: height error 
K: constant term 
E: signal to noise ratio 
Note that only a correlation estimate is computed within an 
estimator window from the flattened interferogram (Small et al., 
1995). Therefore, the estimated height error is dependent on the 
size of the estimator window (Prati et al., 1994). Still, the 
correlation coefficient can be used as a confidence map of the 
InSAR DEM.
	        

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baltsavias, emmanuel p. Fusion of Sensor Data, Knowledge Sources and Algorithms for Extraction and Classification of Topographic Objects. RICS Books, 1999.
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