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 Figure 1: a) Aerial image b) Edge extraction result. Grey level edges obtained with FEX. Observe the large number of small edges in the textured areas, c) Texture edges from (Shao and Forstner, 1994). program FEX but with a newly developed filter bank. The resulting multichannel image represents the scale charac teristics of the local Autocovariance Function (SCAF) (cf. Fig. 3). Thus, we apply FEX on a suitable representation of texture instead of a grey level or color image for obtaining texture edges. We do not aim at a a complete representation of the tex tures. However, we achieve a separation of neighboring textured areas, that is sufficiently good for the interpreta tion of aerial images. Figure 2: Extension of FEX by the scale characteristics of the local autocovariance function (SCAF). After a normalization step, we combine the pyramid levels for all three texture features in a multichannel image (sec tion 4.4). In the second step, we extract the texture edge (section5) using the multichannel scheme known from our feature ex traction. grey level, multichannel image r A FEX V y segmented image e.g. 5 levels for Laplacian Pyramid p ... negative Hessian of autocovariance function SCAF... scale characteristics of autocovariance function Figure 3: Process to obtain SCAF (scale characteristics of the local autocovariance function) and the texture segmen tation from a grey level or multichannel image using FEX. 3 OVERVIEW We want to give an overview of the individual steps of our texture edge extraction scheme (cf. Fig.3). First, we derive the scale characteristics of the local auto covariance function, (cf. section 4). This is the basic step of our approach. These characteristics are derived in two steps: • the strength, direction and anisotropy of the texture are derived from the square gradient of the image function (4.2). They characterize the form of the local autocor relation function. This way we obtain three features of the image texture at the highest resolution. • the spatial frequency of these features is then deter mined using a Laplacian pyramid (4.3). 4 SCALE CHARACTERISTICS OF LOCAL AUTOCOVARIANCE FUNCTION This section explains in detail the steps for derivation of the scale characteristics of the local autocovariance function. 4.1 Stochastic image model To characterize the textures we have used the following im age model. Starting from a fully partitioned image (I = (J™ x Si) into m segments Si, we assume that the ideal image function within the segments is a weak stationary process f t (r, c) ~ N(m,Ci), where m — const and Q is the covariance matrix of the image pixels within the segments (r rows, c columns). We assume the covariance matrix to be repre sentable by a p. d. covariance function C(Ar, Ac), thus

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