Object-based classification of remote sensing data for change detection

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 58, Issue 3-4, 2004, Pages 225-238

  Walter, Volker ^a  

^a UNIVERSITY OF STUTTGART   (Germany)

Author keywords

Change detection; Classification; Data fusion; Object oriented image analysis

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMMUNICATION CHANNELS (INFORMATION THEORY); DATA REDUCTION; GEOGRAPHIC INFORMATION SYSTEMS; LAND USE; MAXIMUM LIKELIHOOD ESTIMATION; MULTISPECTRAL SCANNERS;

MAXIMUM LIKLIHOOD CLASSIFICATION;

REMOTE SENSING;

DATA ASSIMILATION; GIS; IMAGE CLASSIFICATION; LAND USE; MAXIMUM LIKELIHOOD ANALYSIS; PIXEL; REMOTE SENSING; SPATIAL DATA;

EID: 1642528357     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2003.09.007     Document Type: Article

References (14)

1. Aplin P., Atkinson P., Curran P. Per-field classification of landuse using the forthcoming very fine resolution satellite sensors: problems and potential solutions. Atkinson P., Tate N. Advances in Remote Sensing and GIS Analysis. 1999;219-239 Wiley, Chichester.
2. Arbeitsgemeinschaft der Vermessungsverwaltungen der Länder der Bundesrepublik Deutschland (AdV) Amtlich Topographisches-Kartographisches Informationssystem (ATKIS). Landesvermessungsamt Nordrhein-Westfalen, Bonn. 1988.
3. Blaschke T., Lang S., Lorup E., Strobl J., Zeil P. Object-oriented image processing in an integrated GIS/remote sensing environment and perspectives for environmental applications. Cremers A., Greve K. Environmental Information for Planning, Politics and the Public. vol. II:2000;555-570 Metropolis-Verlag, Marburg.
4. Campbell J.B. Introduction into Remote Sensing. 1987;The Guildford Press, New York.
5. Dupas C.A. SAR and LANDSAT TM image fusion for land cover classification in the Brazilian Atlantic Forest Domain. International Archives for Photogrammetry and Remote Sensing. XXXIII(Part B1):2000;96-103.
6. Haala N., Walter V. Classification of urban environments using LIDAR and color aerial imagery. International Archives for Photogrammetry and Remote Sensing. XXXII(Part 7-4-3W6):1999;76-82.
7. Hahn M., Stallmann D., Staetter C. The DPA-sensor system for topographic and thematic mapping. International Archives of Photogrammetry and Remote Sensing. XXXI(Part B2):1996;141-146.
8. Heipke C., Straub B.-M. Relations between multi scale imagery and GIS aggregation levels for the automatic extraction of vegetation areas. Proceedings of the ISPRS Joint Workshop on "Sensors and Mapping from Space," Hannover:1999;. On CD-ROM.
9. Konecny G. Hochauflösende Fernerkundungssensoren für kartographische Anwendungen in Entwicklungsländer. ZPF. 64(2):1996;39-51.
10. Pohl C., van Genderen J. Multisensor image fusion in remote sensing: concepts, methods and applications. International Journal on Remote Sensing. 19(5):1998;823-864.
11. Walter V. Automatic classification of remote sensing data for GIS database revision. International Archives for Photogrammetry and Remote Sensing. XXXII(Part 4):1998;641-648.
12. Walter V. Comparison of the potential of different sensors for an automatic approach for change detection in GIS databases. Lecture Notes in Computer Science, Integrated Spatial Databases: Digital Images and GIS, International Workshop ISD '99. 1999;47-63 Springer, Heidelberg.
13. Walter V. Automatic change detection in GIS databases based on classification of multispectral data. International Archives of Photogrammetry and Remote Sensing. XXXIII(Part B4):2000;1138-1145.
14. Walter V., Fritsch D. Automatic verification of GIS data using high resolution multispectral data. International Archives of Photogrammetry and Remote Sensing. XXXII(Part 3/1):2000;485-489.