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 65 Figure 6: Scheme for generating the Laplacian box. Upper row: generated Gaussian pyramid, lower row: generated Laplacian pyramid. The resampling factor ki for the Gaussian pyramid levels i generated before is k{ = 2*. Because we are interested in a uniform size of all generated levels of the Laplacian pyramid, our resampling factor differs to the factor used in (Burt and Adelson, 1983). Due to the uniform size of the generated images in the different levels, we call this stack Laplacian box. 4.4 Normalization for generation a multichannel im age In section 4.3 we discussed the use of the scale space in order to obtain a rich image description. Our next step is to specify this predicate. In the segmentation process we use all generated resolution levels in parallel. Combining all levels U (0 < i < N) of the Laplacian box of all texture parameters, we get a multichannel image, thus we actually stack the three Laplacian boxes. In order to be able to fuse the different channels of the Laplacian box, we need to normalize these channels. For the normalization, we use the expected noise behavior of the filter kernels of the Laplace box, which we determine by analyzing the impulse response, based on the linearity of the generation process: If a filter h{r.c) is applied to an image g(r,c) with white noise n(r,c) ~ N(0,<rl), the noise variance <j 2 n , of the re sulting image g'{r,c) = h(r, c) * g(r, c) is given by: °n' = O’r Therefore the influence factor of the filter operation is the to tal of the squares of the filter coefficients. This corresponds to the proposal of (Ballard and Rao, 1994) who take the total energy of the filters. For our specific case, the analy sis of the impulse response of the levels U of the Laplacian box, generated using the binomial mask B A (Jàhne, 1989), we get the normalization factor /¿: /»■=-?-=£ h 2 { (r,c) The normalization factors only depend on the used filter mask. Fig. 7 shows all channels of the feature space for the texture edge extraction. Figure 7: Feature space for texture edge extraction. From top to down the aerial image with strength, anisotropy and direction of the texture respectively, and from left to right the levels of the Laplacian box for each feature. 5 TEXTURE EDGE EXTRACTION The final task is the extraction of texture edges. 5.1 Edge detection We use the feature extraction program FEX to extract the texture edges. This program analyzes the local autocovari ance function of a multichannel image g using the negative Hessian Tg, in our specific case T(SCAF). Using FEX for edge detection, results in texture edges. These edges separate neighboring textured areas depend on the user- selectable parameters of FEX (resolution scale, scale for lines and a significance level for internal statistical tests). Altogether, we need to specify five parameters: 1. The differentiation scale si, needed for determining the texture properties at the highest resolution.

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