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 AUTOMATIC CLASSIFICATION OF URBAN ENVIRONMENTS FOR DATABASE REVISION USING LIDAR AND COLOR AERIAL IMAGERY N. Haala, V. Walter Institute of Photogrammetry, University of Stuttgart, Geschwister-Scholl-Str. 24, Stuttgart, D-72110, Germany, Norbert.Haala@ifp.uni-stuttgart.de, Volker.Walter@ifp.uni-stuttgart.de KEYWORDS: Classification, Building Extraction, Data Fusion, Laser Scanning. ABSTRACT This article describes the combination of Digital Surface Models (DSM) resulting from airborne laser scanning and colour aerial imagery for landuse classification in urban environments. The laser DSM is used to obtain information on the local height above the terrain surface for each pixel. This information is used in a following step in order to separate objects higher than the terrain like buildings and trees from objects which are at terrain level, like streets and grass-covered areas. The basic idea of the proposed algorithm is to combine this geometric information with additional multispectral information provided from colour aerial imagery. Both types of information are integrated in a pixel-based classification, where the information on the local height above the terrain is integrated as an additional channel together with the spectral channels. It is demonstrated that, by this approach, the classification of urban scenes can be improved considerably compared to a scene labelling, where only one type of data is applied. 1. INTRODUCTION Spectral information has been widely used as a data source for thematic mapping applications. Surface material information can be derived by traditional classification techniques from multispectral imagery and can for example be used for mapping of man-made structures and natural features in complex urban scenes. During the past years, numerous classification algorithms have been developed for thematic mapping applications. They can be divided into unsupervised and supervised approaches. During an unsupervised classification, pixels are grouped into different spectral (and textural) classes by clustering algorithms without using prior information. After clustering, the spectral classes have to be associated with the land cover classes by an operator. In a supervised classification two basic steps are carried out. First, in a training stage an operator digitises training areas that describe typical spectral and textural characteristics of the dataset. In the following classification stage, each pixel of the dataset is assigned to a land cover class. For this classification stage, a lot of different approaches such as minimum-distance, parallelepiped or maximum likelihood classification are available (Lillesand and Kiefer (1994)). The main problem during thematic mapping of man-made structures and natural features is the limited accuracy and reliability of the results, as well as the frequently arising difficulties to discriminate a sufficient number of object categories. A common goal during data acquisition in built-up areas is the detection of objects like streets and buildings. However, this can be difficult if only spectral information is used, since, for some areas, roofs and streets are built of very similar material. This complicates or even prevents the discrimination of these objects due to their similar reflectance. The same problem can arise, if trees and grass-covered areas have to be differentiated. Traditionally, multispectral data has been the dominating source of information for landuse classification. Usually, the input data for the classification are multispectral images. Optionally, textural patterns, which are derived from the original spectral data, can be included. Up to now, auxiliary information on the surface topography has been mainly used for the geometric correction of the spectral data by the generation of ortho images. Additionally, surface topography information has been used for the radiometric correction of the multispectral imagery. This can be necessary, especially in hilly or mountainous terrain, since spectral reflectance values are influenced by the inclination and orientation of the terrain surface. In contrast to this, in our approach height data is not only applied for correction of the multispectral data, but also integrated as a complementary data source of equally important information. During data acquisition in urban environments, trees and buildings are object classes of major interest since these objects are very relevant for many applications like visualisations or simulations of the propagation of electro-magnetic radiation. In order to separate these types of objects from their surrounding, information on the local height above terrain can be very valuable since trees and buildings are higher than then- surrounding, whereas other objects of interest like streets and grass-covered areas are at the terrain level. The required information can be derived at least approximately from a DSM, which for our application is acquired by airborne laser scanning. Based on this DSM, the local height above terrain of the visible surface is derived. The result consists of all objects rising above the terrain approximately put on a plane. This surface is combined with the multispectral bands during a standard classification in order to improve the extraction of the required object classes.

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