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:: INTERACTIVE SESSION 1 IMAGE CLASSIFICATION

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: AN AUTOMATED APPROACH FOR TRAINING DATA SELECTION WITHIN AN INTEGRATED GIS AND REMOTE SENSING ENVIRONMENT FOR MONITORING TEMPORAL CHANGES. Ulrich Rhein

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 meadow meadow to farmland farmland Ä; farmland to meadow Fig. 5. Change detection analysis from 1991 to 1995. but differ thematically, e.g., if we use a unimodal forest training boundary with a new scene and we find at the same area meadowland also with a unimodal behaviour. To exclude such errors, after a change detection analysis we detect the training areas with changes and eliminate them from the database. Of particular importance is a large size for each training area. If an automated inside buffering reduces a training area to a size that is no longer useable, this area has to be eliminated. The size of the training set for each class in the classification should be at least 30 times the number of discriminating variables (e.g., bands) in the analysis (Swain and Davis, 1978). The next step is a classification strategy adjusted especially for monitoring analysis. We use common robust classification algorithms (e.g. Maximum Likelihood) in a knowledge-based hierarchical process to discriminate between the major landcover types (Rhein and Ehlers, 1996). After the first classification pass (after the first date), the results can be used to generate new training sets to be stored in our information database. Improvement of accuracy is the ultimate goal, which we try to reach through our methods. To measure the accuracy, we not only use standard accuracy assessment methods (e.g., contingency matrices). Use is also made of per-pixel values of the Mahalanobis distance corresponding to the highest membership. This technique is mentioned also at D’ Urso and Menenti, 1996. Overlay and mask techniques, as well as GIS database queries, make it possible to eliminate those pixels whose confidence level is lower than a predefined one. 5.2. First Results Using the above techniques, a first test of our method was carried out to analyse changes in agriculture areas. Figure 5 shows the change detection analysis within a farmland area. Changes have occurred from meadowland to farmland and vice versa from the year 1991 to 1995. 6. CONCLUSIONS First steps of an automated approach for training data selection within an integrated GIS and remote sensing environment for monitoring temporal changes are presented. The future activities will focus on the integration and automation of the classification strategies. An extension to multimodal classes, like urban areas, will be aimed at. Beyond this, a more formal statistical and quantitative approach concerning training classes has to be addressed. REFERENCES Anderson, J. R., Hardy, E. E., Roach, J. T., Witmer, R.E., 1973. A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964, U.S. Government Printing Office, Washington, D.C., 28 p. Argialas, D. and C. Harlow, 1990. Computational Image Interpretation Models: An Overview and Perspective. Photogramme trie Engineering & Remote Sensing, Vol. 56, No. 6, pp. 871-886. Bähr, H. and T. Vögtle (Eds.), 1991. Digitale Bildverarbeitung: Anwendung in Photogrammetrie, Kartographie und Fernerkundung. Karlsruhe, Wichmann. Campbell, J., 1996. Introduction to Remote Sensing. Guilford Press, New York. D’ Urso, G. and M. Menenti, 1996. Performance indicators for the statistical evaluation of digital image classifications. ISPRS Journal of Photogrammetry & Remote Sensing, 51(2), pp. 78-90. Drachenfels, O. v. (Bearb.), 1994. Kartierschlüssel für Biotop-Typen in Niedersachsen unter besonderer Berücksichtigung der nach § 28a und § 28b NNatG geschützten Biotope, Stand September 1994. Sonderreihe A: Ausgewählte Grundlagen und Beispiele für Naturschutz und Landschaftspflege, Heft A/4. Published by Niedersächsisches Landesamt für Ökologie, Hildesheim, 192 p. Ehlers, M., 1996. Remote Sensing and Geographie Information Systems: Advanced Technologies for Environmental Monitoring and Management. In: Singhroy, V.H., D.D. Nebert, and A.I. Johnson (Eds.), Proc. of ASTM Int'l. Symp. on Remote Sensing and GIS for Site

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