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:: INTEGRATION OF DTMS USING WAVELETS. M. Hahn, F. Samadzadegan

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 INTEGRATION OF DTMS USING WAVELETS M. Hahn ', F. Samadzadegan 2 Department of Surveying and Geoinformatics, University of Applied Sciences Stuttgart, Schellingstr. 24, D-70174 Stuttgart, Germany, m.hahn.fbv@fht-stuttgart.de 2 Faculty of Engineering, University of Tehran, P.O.Box 11365-4563 Tehran, Iran KEYWORDS: Wavelet Analysis, DTM Integration, Fusion, Multiscale Processing, Filtering ABSTRACT The number of sensors and techniques for the acquisition of Digital Terrain Models (DTMs) and the variety of applications based on DTMs have grown significantly during the last decade. As a result of that, DTMs exist with different spatial resolution and accuracy. For some areas, quite a number of DTMs is available or there is the task to update existing DTMs with new measurements, e.g. of higher accuracy. For merging given DTMs of a certain area, different possibilities exist, even though from a theoretical point of view not much attention has been paid to this topic so far. Probably the simplest procedure is to introduce the raster points of different DTMs jointly as observations into an interpolation process. In this paper, we present a new concept for the integration of DTMs based on wavelets. Wavelets can contribute to tasks for which multiscale aspects are essential. The concept considers two or more DTMs which may differ in resolution and accuracy. The three most important components of our DTM integration approach are the following: by wavelet decomposition and multiscale processing a DTM and its accuracy can be represented in a series of scales. Merging of two DTMs takes place on the same scale level, e.g. by a least squares fit. Finally, an integrated high resolution DTM is obtained by a wavelet reconstruction process. First experiments have been carried out to illustrate the procedure and gain insight into the achieved accuracy improvements of integrated DTMs. 1. INTRODUCTION In recent years, wavelets have taken over a leading role in analysis and synthesis of signals. The wavelet transform is widely applied, e.g. in image processing for data compression, data de-noising and feature extraction but also in many other areas. Wavelet-based approaches show some favourable properties compared to the Fourier transform. The Fourier transform uses a complex exponential function to transform a signal into the frequency domain. The plane waves oscillate infinitely with the same period and do not allow localisation. Changing a signal in just one data point results in a change of the whole spectrum of this signal without a possibility to estimate the location of the change in the signal from the spectrum. A wavelet can be interpreted as a "small wave” or a generalised oscillation. In the wavelet transform, a shifted and dilated wavelet is used to analyse the signals. Thus, the wavelet transformation can be interpreted as a convolution or correlation of the signal with scaled versions of the wavelet. The degree of similarity between a part of the signal at a certain location and the scaled version of the wavelet gives the key input for feature extraction. Threshold strategies for the wavelet coefficients with small absolute values lead to efficient data compression with high compression rates, as well as to noise reduction schemes. For examples, please cf. Thierschmann, et al., 1997 and Pan and Schaffrin, 1999. Another important issue for the integration of DTMs are the conceptual developments in the area of data integration or data fusion. It is helpful to have an understanding of the methods and goals of integration or fusion. The following table is a shortened excerpt taken from Abidi and Gonzalez (1992): Signal/Pixel Feature level Symbol level level Methods estimation / correspondence, logical / combination attribute statistical combination inference Improve- of expected increased feature increase in ment variance, measurement, truth or in increase quality, value of addi- probability performance tional features values Table 1. Data fusion (adopted from Abidi and Gonzalez, 1992, p. 37). On the feature level, the goal of fusion is to increase the feature measurement with regard to quantity or diversity. The symbol level pursues decision-related goals by aiming at an improvement of probabilistic decisions based on inference techniques. The situation on the signal level can be characterised by a wide range of estimation techniques which typically aim at improving the accuracy or more generally the quality of a state. The integration of DTMs falls under the category of fusion on the signal level. Our integration method consists of multiscale signal processing based on wavelets. Localisation provides the theoretical background that DTM information of multiple scales is combined according to its local spectral property. The goal is

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