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 155 Year Acquisition date of remote sensing imagery Ground truth information SPOT Landsat 5-TM (Scene 196-23) Information from the Environmental Protection Agency - district Diepholz 1984 25.04.1984 aerial photographs 1984 1986 17.05.1986 1987 07.07.1987 CIR- aerial photographs 26 May 1987 1988 biotope type mapping Diepholz 1988/89 1990 15.07.1990 1991 13.04.1991 biotope type mapping Bassum 1991 1992 biotope type mapping Bamstorf 1992 1994 18.02.1994 24.06.1994 biotope type mapping Bruchhausen-Vilsen 1994 26.07.1994 biotope type mapping Bruchhausen-Vilsen 1994 1995 24.04.1995 27.06.1995 Table 1. Overview of the current database. The light grey rows show the most relevant data. 3. GROUND TRUTH To yield acceptable classification results, training data is needed. Training statistics for all spectral classes constituting each information class have to be computed. A supervised classification requires a thorough knowledge of the geographic area. Most importantly, the quality of the training process determines the success of the classification stage and, therefore, the value of the information generated from the entire classification effort. The overall objective of the training process is to assemble a set of statistics that describe the spectral response pattern for each landcover type to be classified in an image (Lillesand and Kiefer, 1987). An objective mapping key for ground truth measurements was generated. This mapping key is adapted to the special biotope mapping key in Lower Saxony with respect to § 28a NNatG (Lower Saxony's Environmental Protection Statutes) (Drachenfels, 1994). Moreover, it was adjusted to the German ATKIS object catalogue. For selected queries and overlay analysis, these mapped test areas were transformed to separate GIS layers in Arclnfo® format. Figure 1 shows the total test area (which corresponds to the district boundary of Diepholz) and the 4 ground truth locations. The box demonstrates the exact dimension of the central LandsatTM scene 196-23. Fig. 1. Overview of the total test area and ground truth areas. The box shows the outline of Landsat TM scene 196-23.

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