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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 3 OBJECT AND IMAGE CLASSIFICATION
Document type:
Monograph
Structure type:
Chapter

Chapter

Title:
SCALE CHARACTERISTICS OF LOCAL AUTOCOVARIANCES FOR TEXTURE SEGMENTATION. Annett Faber, Wolfgang Förstner
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 
Figure 1: a) Aerial image b) Edge extraction result. Grey 
level edges obtained with FEX. Observe the large number 
of small edges in the textured areas, c) Texture edges from 
(Shao and Forstner, 1994). 
program FEX but with a newly developed filter bank. The 
resulting multichannel image represents the scale charac 
teristics of the local Autocovariance Function (SCAF) (cf. 
Fig. 3). Thus, we apply FEX on a suitable representation of 
texture instead of a grey level or color image for obtaining 
texture edges. 
We do not aim at a a complete representation of the tex 
tures. However, we achieve a separation of neighboring 
textured areas, that is sufficiently good for the interpreta 
tion of aerial images. 
Figure 2: Extension of FEX by the scale characteristics of 
the local autocovariance function (SCAF). 
After a normalization step, we combine the pyramid levels 
for all three texture features in a multichannel image (sec 
tion 4.4). 
In the second step, we extract the texture edge (section5) 
using the multichannel scheme known from our feature ex 
traction. 
grey level, multichannel 
image 
r 
A 
FEX 
V 
y 
segmented image 
e.g. 5 levels for Laplacian Pyramid p ... negative Hessian of 
autocovariance function 
SCAF... scale characteristics 
of autocovariance function 
Figure 3: Process to obtain SCAF (scale characteristics of 
the local autocovariance function) and the texture segmen 
tation from a grey level or multichannel image using FEX. 
3 OVERVIEW 
We want to give an overview of the individual steps of our 
texture edge extraction scheme (cf. Fig.3). 
First, we derive the scale characteristics of the local auto 
covariance function, (cf. section 4). This is the basic step 
of our approach. These characteristics are derived in two 
steps: 
• the strength, direction and anisotropy of the texture are 
derived from the square gradient of the image function 
(4.2). They characterize the form of the local autocor 
relation function. This way we obtain three features of 
the image texture at the highest resolution. 
• the spatial frequency of these features is then deter 
mined using a Laplacian pyramid (4.3). 
4 SCALE CHARACTERISTICS OF LOCAL 
AUTOCOVARIANCE FUNCTION 
This section explains in detail the steps for derivation of the 
scale characteristics of the local autocovariance function. 
4.1 Stochastic image model 
To characterize the textures we have used the following im 
age model. 
Starting from a fully partitioned image (I = (J™ x Si) into 
m segments Si, we assume that the ideal image function 
within the segments is a weak stationary process f t (r, c) ~ 
N(m,Ci), where m — const and Q is the covariance 
matrix of the image pixels within the segments (r rows, c 
columns). We assume the covariance matrix to be repre 
sentable by a p. d. covariance function C(Ar, Ac), thus
	        

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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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