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

baltsavias, emmanuel p.

Bibliographic data

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 1 OVERVIEW OF IMAGE / DATA / INFORMATION FUSION AND INTEGRATION

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: INTEGRATION OF IMAGE ANALYSIS AND GIS. Emmanuel Baltsavias, Michael Hahn,

Document type:: Monograph

Structure type:: Chapter

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 4. Investigation and development of techniques for geocoded multisensor data fusion; 5. Use of image analysis techniques to extract height information for 2D databases; 6. Integration of image analysis techniques with GIS for querying, analysis and representation of spatial data; 7. Treatment of uncertainties, generalisation, and scale and temporal differences of GIS and image derived data. A topic, which is not included in the above ToR but increasingly attracts the attention of scientists, is the integration of various cues, and of different algorithms and their partial results for object recognition and reconstruction. Among the ToR, the major activities of the WG and its 90 members since 1996 have been primarily on topics 1, 2, 4 and less 5. Topic 4 has been treated extensively in many papers of these proceedings (for an overview and comparison of various methods see Pohl, 1999; Hill et al., 1999), so our focus in this paper will be on the remaining 3 topics. We will concentrate on aerial and spacebome sensors and as tasks, classification, identification and reconstruction of topographic objects both in 2-D and 3-D, using semi-automated and automated methods. 2. RESULTS AND OVERVIEW OF A SURVEY A questionnaire was sent to our WG members in 1998 to collect information on their research activities, publications, applications, interests etc. More than 100 references were received. The above information was appended by an additional, not complete, search of the authors and will be presented here. Other work on similar and related topics is performed by additional ISPRS WGs (e.g. H/2, II/6, III/3, III/4, III/5, IV/2, IV/3, VII/4; information on these WGs can be found at www.geod.ethz.ch/isprs), the Special Interest Group Data Fusion’ (www-datafusion.cma.fr/sig/) of EARSeL and the proceedings of the associated "Data Fusion" conferences, and some OEEPE WGs (www.itc.nl/~oeepe), e.g. the WG on Automatic Absolute Orientation on Database Information (www.i4.auc.dk/jh/OEEPEgroup.htm). The responses showed that the developments in this field, although continuously increasing, are quite fragmented and in various heterogeneous fields and applications. A clear overview of these developments and underlying unifying theories is missing. The expectations and the interest from people involved in production were high. A representative answer was "As a National Mapping Agency ...very interested..., especially if it led to systems which we could use in our day-to-day activities of maintaining geospatial datasets". The major applications addressed were the following: • Fusion of panchromatic and spectral data (SPOT, IRS, Landsat, MOMS, DP A); often used for improvement of visual interpretation in application-specific environments, e.g. in forestry or military reconnaissance (Zhukov et al., 1995; Garguet-Duport et al., 1996; Wald et al., 1997; Steinnocher, 1999; Pohl and Touron, 1999). • Fusion of optical images and SAR (e.g. hybrid orthoimages) (Pohl, 1996) or of products derived from them, like DTMs (Honikel, 1999). • Use of GIS/maps for automatic DTM generation, image segmentation and object extraction (especially buildings, roads, landcover classes), and generation of 3-D city models (van Cleynenbreugel et al., 1990; Janssen et al., 1990; Solberg et al., 1993; Maitre et al., 1995; Quint and Sties, 1995; de Gunst, 1996; Quint and Landes, 1996; Roux et al., 1996; Roux and Maitre, 1997; Bordes et al. 1997; Haala et al., 1997; Huang and Jensen, 1997; Koch et al., 1997; Stilla et al., 1997; Schilling and Vogtle, 1997; Tonjes, 1997; Quint, 1997a, 1997b; Prechtel and Bringman, 1998; Zhang, 1998; Stilla and Jurkiewicz, 1999). • Integration of image and map data in GIS (e.g. for a forest information system) (Dees and Koch, 1997). • Use of GIS for automatic training area selection and verification of the results in landcover and landuse classification, especially in agriculture and forestry (Walter and Fritsch, 1998). • Use of ortho- or normal images in GIS for updating of topographic or thematic maps (Duplaquet and Cubero-Castan, 1994; Newton et al., 1994; Plietker, 1994; Aas et al., 1997; Vosselman and de Gunst, 1997; Duhaime et al., 1997; Peled and Haj-Yehia, 1998; Walter and Fritsch, 1998). Usually the updating is done manually using orthoimages as a backdrop, but steps towards automation in national mapping organisations (Israel, IGN, Canada planned) have been performed. • Use of GCPs from maps/digital databases and their detection in images, or use of orthoimages and DTMs for automatic image orientation and geocoding (Pedersen, 1997; Drewniok and Rohr, 1997; Hoehle, 1998, Sester et al., 1998); related to the topic below. • Automatic registration of images to images, (vector) maps and models (Roux, 1996; Growe and Tonjes, 1997; Ely and Di Girolamo, 1997; Dowman and Ruskoné, 1997; Vasileisky and Berger, 1998; Dowman and Dare, 1999). • Registration of images to site-models (similar to previous topic, but related more to change detection, often in the context of military applications) (Chellapa et al., 1994; Mueller and Olson, 1995; Huertas et al., 1995). • Use of image analysis for automatic interpretation and vectorisation of maps (Frischknecht et al., 1998). • Use of image analysis in data mining, image retrieval and queries (Agouris et al., 1998). • Matching of maps and vector datasets (often termed "conflation"), e.g. for combination of one road vector dataset with good geometry with another one having poor geometry but rich and up-to-date attributes. • Use of image analysis to extend existing spatial databases from 2D to 3D (Axelsson, 1997; Lammi, 1997). • Combination of different cues (indicators) for classification, object recognition and reconstruction (multi- and hyper- spectral properties, texture, 3D form from DSMs and DTMs derived from imagery or laser scanners, morphology and shape, shadows etc.) (Solberg et al., 1994, 1996; Moissinac et al., 1995; Strat, 1995; Henricsson et al., 1996; Baltsavias and Mason, 1997; Baumgartner et al., 1997; Bruzzone et al., 1997; Stolle et al., 1997; Lemmens et al., 1997; Piesbergen and Haefner, 1997; Hahn and Statter, 1998; Csathó et al., 1999; Haala and Walter, 1999).

Access restriction

Copyright

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

Monograph

Chapter

Chapter

Table of contents

Cite and reuse

Monograph

Chapter

Image

Image fragment

Citation links

Citation recommendation