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 7 APPLICATIONS IN FORESTRY

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: OBJECT-BASED CLASSIFICATION AND APPLICATIONS IN THE ALPINE FOREST ENVIRONMENT. R. de Kok, T. Schneider, U. Ammer

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 199 OBJECT-BASED CLASSIFICATION AND APPLICATIONS IN THE ALPINE FOREST ENVIRONMENT R. de Kok, T. Schneider, U. Ammer Chair for Landuse Planning and Environmental Protection, Faculty of Forest Science, University of Munich, Am Hochanger 13, D- 85354 Freising, Germany, ammer@abies.lnn.forst.uni-muenchen.de, Roeland.dekok@lrz.uni-muenchen.de KEYWORDS: Image Classification, Context, Segmentation, Fuzzy Logic, Forestry. ABSTRACT Context based classification is an important field of study in digital image analysis. Neighbouring pixels may possibly have almost equal grey values. The information of local homogenous patterns in a patch based landscape organisation has lead to studies, assuming the organisation of landscape patterns as being a complex of local spectral distributions. Image segmentation techniques are well known and an advanced algorithm is used in this study. New sensor generations meet the strong market demands from end- users, who are interested in image resolution that will help them observe and monitor their specific objects of interest. The mixed pixel problem and the increasing difficulties in the spectral analysis of high resolution images make it necessary to develop additional methods of classification. The key factor is to concentrate on the spectral properties of the objects of interest. This has important consequences. The increasing resolution (<5 m) leads to very complex spectral analysis. Fuzzy logic decision rules offer here a large reduction in complexity and a proper aid to group the spatial objects into meaningful classes. In this study, a new software package is used for object-based classification, developed by DELPHI2™ Creative Technologies. With this software, the segmentation procedure has to be set according to the image resolution and the scale of the expected objects. For foresters, the typical spatial object can range from forest-stands to crown surfaces. According to user preferences, objects of interest are grouped into a class. The fuzzy logic decision rules for class membership are the framework in which the expert knowledge has been embedded. The synergy of the spectral properties, the neighbourhood object influences and the expert knowledge lead to powerful ways of object membership decision rules. The fuzzy logic rules guarantee the transparency of the decision rules and reduce complexity to a condensed crisp set of end-membership functions. Integrating GIS layers is equally possible. The output of the object-based classification is typically a GIS layer. 1. INTRODUCTION 1.1. History and user demands Digital image classification has been based upon three principal methods: Pixel based spectral signature, pixel-statistics from GIS objects and context oriented pixel classification (Carl, 1996). Since several decades classification beyond spectral signature alone has been recognised as an important study field. In a digital landscape image, the chance of a pixel containing the same value as its neighbours is much more likely than another random pixel in the image, a feature that is very useful in image compression techniques. Of course, this depends on the relationship between a chosen spatial resolution and the type of landcover. In general, a typical landcover class contains a considerable amount of pixels. The information of local homogenous patterns in a patch based landscape organisation has lead to several studies, assuming the organisation of landscape patterns as being a complex of local spectral distributions, belonging to specific landscape features. Image segmentation techniques have been developed since a few decades and the original work done by Kettig and Landgrebe (1976) and the theories of Cross and Mason (1988) and Gorte (1996) in which the image segmentation philosophy is thoroughly explained still have high theoretical value. In this study, the "Image Analysis" software from DELPHI2™, Creative Technologies (a Munich software firm) is used to explore the possibilities of advanced segmentation and object- oriented applications. End-users are familiar with very high resolution data from aerial photographs, which fulfil most of the user needs. Multispectral image information in more than three bands, however, is only possible with digital scanners. When this information is needed, aerial scanners become much more expensive. The cost of visual interpretation is the bottleneck for an increase in their practical utilisation. Considerable improvements in automatic image analysis are essentially dealing with cost reduction of image interpretation. Meanwhile, the amount of images covering certain parts of the Earth is increasing so rapidly that automatic image analysis offers the sole solution to extract important information. The applications community is interested in image resolutions that will help them observe and monitor their specific objects of interest. The increasing resolution and the physical properties of objects in the images with a geometrical resolution of less than 5 m leads to very complex spectral analysis (Kenneweg et al., 1991). The mixed pixel problem and the increasing difficulties in the spectral analysis of high resolution images make it necessary to develop additional methods of classification. The key factor is to concentrate on the spectral properties of the (spatial) objects of interest, instead of the class statistics of the whole image. This

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