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 4 FUSION OF SENSOR-DERIVED PRODUCTS

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: AUTOMATIC CLASSIFICATION OF URBAN ENVIRONMENTS FOR DATABASE REVISION USING LIDAR AND COLOR AERIAL IMAGERY. N. Haala, V. Walter

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 Another possibility to improve object detection based on DSM resulting from airborne laser scanning is the further analysis of the reflected laser beam. Laser scanning systems can be separated into continuous wave or pulsed laser systems. If a pulsed laser system is applied, multiple reflections will occur during the acquisition of trees. As depicted in Figure 2, during measurement of trees a certain percentage of the laser footprint will be reflected by the branches and leaves of the tree. Other parts will penetrate the foliage and will be finally reflected by the terrain surface. For this reason the top of the tree refers to the first echo of the laser pulse, which is recorded by the laser sensor, while the last echo usually refers to the terrain surface. first response reflections at foliage last response reflection at terrain Fig. 2. Reflection of a laser pulse at trees. Fig. 3. Grey value representation of DSM derived from first echo measurement. If the laser system is capable of recording and discriminating multiple laser pulse echoes, they can be utilized in order to separate trees and buildings. Figure 3 shows a grey value representation of a normalized laser DSM. The original DSM, which was already depicted as 3D visualization in Figure 1, is based on the first echo measurement. For this reason, both trees and buildings are visible. Figure 4 shows the corresponding result for a DSM derived from last echo measurements. In this example, only the buildings are visible. Hence, the difference between first and last echo normalized DSMs can be used for the detection of tree regions. Fig. 4. Grey value representation of DSM derived from last echo measurement. The laser system we are using for DSM acquisition is not capable of simultaneous recording of multiple echoes. Currently, either the first or the last reflection of the emitted laser pulse can be measured. Since this prevents the measurement of the required data in a single pass coverage, the flight effort for laser data capture is doubled, if one aims at the acquisition of the first and last response of the emitted laser pulse. Additionally, in our examples for some areas no response could be measured at all in the last pulse mode. These regions correspond to the white areas depicted in Figure 4. Besides these sensor-related problems, a further differentiation of object classes like the extraction of streets or different landuse classes like grass-covered areas is not possible, if only laser data is applied. For this reason, in our approach the height data is integrated with multispectral imagery within a combined classification step in order to separate the required objects. 3. CLASSIFICATION OF URBAN AREAS 3.1. Spectral Data For the test site, color infrared (CIR) aerial images were available, which were taken at a scale of 1:5000 with a normal angle aerial camera. For digitization, the images were scanned at a resolution of 60 p.m, resulting in three digital images in the spectral bands near infrared, red and green with a pixel footprint of 30 cm. The basic idea of the proposed algorithm is to simultaneously use geometric and radiometric information by applying a pixel-based classification. Within this classification, the normalized DSM is used as an additional channel in combination with the three spectral bands. For integration of different data types, the first problem to be solved is the registration of the datasets. In order to transform the data a common system a colored ortho-image is generated from the

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