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 3 OBJECT AND IMAGE CLASSIFICATION

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: SCALE CHARACTERISTICS OF LOCAL AUTOCOVARIANCES FOR TEXTURE SEGMENTATION. Annett Faber, Wolfgang Förstner

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 SCALE CHARACTERISTICS OF LOCAL AUTOCOVARIANCES FOR TEXTURE SEGMENTATION Annett Faber, Wolfgang Forstner Institute of Photogrammetry, University Bonn, NuBallee 15, D-53115 Bonn, Germany, email: (annett,wf)@ipb.uni-bonn.de KEY WORDS: texture representation, Laplacian pyramid, spectral decomposition, eigenvalues, urban structure. ABSTRACT This paper describes research on the extraction of urban structures from aerial images or high resolution satellite scenes like the German MOMS02 sensor. We aim at a separation of neighboring textured regions important for describing different urban structures. It has been shown, that grey level segmentation alone is not sufficient to solve this problem. If we want to use texture additionally, the development of a suitable representation of texture images is required. We use the scale characteristics of the local autocovariance function, called SCAF, of the possibly multiband image function. The final result of the process are texture edges. 1 INTRODUCTION Texture is one of the most fundamental and at the same time most interesting characteristics of visible surfaces in the human perception process. Therefore, in pattern recog nition the analysis of textures is very important. Numerous techniques for texture analysis have been proposed. They can be mainly categorized as (Haralick and Shapiro, 1992): 1. Texture classification: For a given textured region, de cide, to which one out of a finite number of classes the region belongs. 2. Texture synthesis: For a given textured region, deter mine a description or a model. 3. Texture segmentation: In a given image, which con tains many textured regions, determine the boundaries between these regions. The procedures developed for the solution of these prob lems can be subdivided into three categories: structural ap proaches, statistical approaches and filterbased approaches (J.-R de Beuaville and Langlais, 1994, Reed and du Buf, 1993, Shao and Forstner, 1994). Structural approaches assume that textures contain de tectable primitive elements which generate the texture in a regular or irregular manner, following certain con nection rules. Such approaches use e. g. so-called texture grammars (Carlucci, 1976) or texture elements (Julez and Bergen, 1983). Due to the almost unlimited number of possible unit patterns and the complexity of the rules, these proce dures have shown lower success in texture analysis than the following two approaches. Statistical approaches use statistical characteristics, deriv able from the images. These characteristics are often sufficient for texture classification and segmentation, without needing generation rules for the textures. They can further be separated into approaches which use • descriptions derivable directly from the images such as variance, entropy or other values ob tained from the local pixel neighborhood, as e. g. co-occurrences (Haralick, 1979) and • model based descriptions, such as autoregres sive models, Markov or Gibbs random fields (An- drey and Tarroux, 1996, Derin and Cole, 1986). Statistical approaches usually refer to the image grid, thus have difficulties in handling scale space proper ties. Filter based approaches assume that the image function can be described locally by its amplitude spectra. Ga bor wavelets have been the first choice together with linear and nonlinear post processing steps to achieve multi scale features in the different channels (A. C. Bovik and Geisler, 1990, Bigun and du Buf, 1992, Malik and Perona, 1990). This class of approaches is motivated by their similarity to the human visual system. The advantage is that the filter responses for basic ge ometrical transformations are predictable and the fil ters work equally for natural scenes of different texture types. However, there is no general approach for the selection of a suitable filter bank and for the linkage of different image channels. 2 MOTIVATION We are interested in analyzing satellite and aerial images especially for extracting urban structures. Therefore, we need powerful techniques for image segmentation in the presence of natural textures. Grey level or color segmen tation results often suffer from over or under segmentation. Texture segmentation can reduce the amount of over seg mentation, mostly without the risk of under-segmentation. Our first experiences with Gabor wavelets based on (Ma lik and Perona, 1990) demonstrated the feasibility of a fil ter based approach for texture edge extraction (Shao and Forstner, 1994). Our approach used the edge detection scheme in ourfeature extraction program FEX (Fuchs, 1998), cf. Figure 1c). It exploits its ability to handle multichannel images by taking the filter responses as a multichannel im age as input. Due to the heavy algorithmic complexity, we develop a new filter based scheme for deriving texture edges again us ing the edge extraction scheme of our feature extraction

Access restriction

Copyright

fullscreen: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

Monograph

Chapter

Chapter

Table of contents

Cite and reuse

Monograph

Chapter

Image

Image fragment

Citation links

Citation recommendation