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:: LANDCOVER MAPPING BY INTERRELATED SEGMENTATION AND CLASSIFICATION OF SATELLITE IMAGES. W. Schneider, J. Steinwendner

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 48 from the edges. The criterion of a simple form of boundaries can easily be put into effect. It is a disadvantage of purely edge- based segmentation methods that the homogeneity of the segments is not guaranteed automatically. Various combinations of region-based and edge-based concepts for segmentation exist. E.g. region growing may be performed with a boundary criterion in addition to the homogeneity condition. Watershed segmentation (Vincent and Soille, 1991) also uses edge information. An edge image obtained by applying a gradient operator is considered as a terrain relief, with high pixel values (edges) representing ridges and areas of low pixel values representing valleys and basins. A catchment basin is defined around each local minimum of this edge image as the set of all pixels that can be connected with the minimum pixel by a path descending from the pixel to the minimum pixel. The catchment basins found in this way represent segments. In order to obtain satisfactory results, a smoothing filter has to be applied before the gradient filter, and neighbouring resulting segments of similar mean pixel values have to be merged. The degree of smoothing and the similarity criterion used in the merging step control the mean size of the final segments. Various methods of segmentation of Landsat TM images for landcover mapping purposes have been tested. The results have been compared to delineations prepared by visual interpretation. As illustrated in Figures 2 and 3, different segmentation methods produce different delineations of the regions. In general, region growing gives the most satisfactory results. The result of the segmentation process may be represented as a labelled image band, the labels counting through the segments, and/or in form of a list, which contains information on the attributes of the segments. Topological information may be provided in a graph structure. Fig. 2. Watershed segmentation. 2.2. Subpixel Methods Depending on the size of the pixels in relation to the spatial details of the scene, mixed pixels occur in the original image in a varying proportion. They may cause problems in the segmentation process. Mixed pixels may form segments on their own, although these segments have no meaning in the scene. One way to deal with the mixed pixel problem is to adapt the segmentation method in order to avoid mixed pixel segments. As an example, a region growing algorithm can be modified in the following way: Firstly, one has to avoid mixed pixels as seed pixels. This can be achieved by selecting as seed pixels low-gradient pixels, or pixels representing local minima or maxima. The next step is to grow regions with rather strict homogeneity requirements. A search for subpixel candidates follows. These are pixels with contiguous segments on opposite sides, where the pixel value in each band lies between the corresponding values of the adjacent segments. These subpixel candidates are assigned to the spectrally nearest segment (although they do not comply with the similarity criterion). This algorithm is applied in the method described in chapter 4. The problems with mixed pixels can also be alleviated by applying subpixel analysis as a preprocessing step, before segmentation. Spectral and spatial subpixel analysis may be distinguished. Spectral subpixel analysis tries to decompose pixel vectors into prototype pixel vectors (Settle and Drake, 1993). Spectral subpixel analysis yields information on the fractions of individual categories (corresponding to the prototypes) within every pixel, but no information on the spatial arrangement within the pixels. The number of prototypes has to be smaller than the number of (linearly independent) spectral bands. It is difficult to establish reliable prototypes. Fig. 3. Region growing segmentation.

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