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

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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 7 APPLICATIONS IN FORESTRY

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: OBJECT-BASED CLASSIFICATION AND APPLICATIONS IN THE ALPINE FOREST ENVIRONMENT. R. de Kok, T. Schneider, U. Ammer

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
203
Feature
Val
Color Featues
Charmefl Mean
91.05
Charmed! Mean
1200
Charmed Mean
1200
CharmeW Mean
75.00
Charmed Mean
50.00
Charmefl StdDev.
50 23
Charmed StdDev.
0.00
Charmed StdDev.
0.00
CharmeW StdDev.
0.00
Charmed StdDev.
0.00
Form Features
Area
52900
Length/Width
1.00
Border Length
9200
Boimdg Box / Object Border
1.00
Number of Ends
moo
Lme SO: length/width
241
LmeSO: width
14.81
Hierarchy Features
Level
1.00
Nun Neighbours
4.00
Num Siiiobjects
33.00
N urn Sub Levels
1.00
The ‘active’ object is displayed as a black square. The pop-up window shows object information, amount of sub-objects (33) and
further statistics of the image bands, e.g. for the panchromatic band a standard deviation of 50.23. Latter is very characteristic for this
type of mountain forest in this particular area.
Channel 1 = panchromatic band (orthophoto with 1 m resolution)
Channel 2-5 = SPOT4 bands 1-4 (pixel size 23x23 m 2 )
Fig. 1. Screen copy of DELPHI2™. The panchromatic image with 1 m resolution is overlaid with the objects of SPOT4. The image
shows a mountainous forest road with forest stands on the left and right side of the road. The image represents 423x512 m .
A grid of SPOT4 pixels registered as outlined spatial objects, each containing 529 (= 23x23) orthophoto pixels, are lying on
top of the sub-objects from the segmentation process using the panchromatic band.

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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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