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 46 LANDCOVER MAPPING BY INTERRELATED SEGMENTATION AND CLASSIFICATION OF SATELLITE IMAGES W. Schneider, J. Steinwendner Institute of Surveying, Remote Sensing and Land Information (IVFL), Universität für Bodenkultur (BOKU, University of Agricultural Sciences) Vienna, Peter-Jordan-Str. 82, A-1190 Vienna, Austria, {schneiwe,joachim}@mail.boku.ac.at KEYWORDS: Landcover Mapping, Optical Satellite Images, Computer Vision, Segmentation, Classification. ABSTRACT Landcover maps produced from satellite images by classical pixelwise statistical classification are less than satisfactory in most cases. One of the reasons for this is that shape information and expert knowledge on the spatial arrangements of the individual landcover patches are neglected. In tin effort to simulate the working method of a human interpreter, image segmentation may be employed in addition to classification. The purpose of image segmentation in general is the delineation of image objects (groups of pixels) with a meaning in the real world. The purpose of image segmentation in landcover mapping is to obtain segments representing patches of distinct landcover, such as agricultural fields, forest stands, rivers, lakes, etc. The problem is the interdependence of segmentation and classification: Classification results are needed as input for a meaningful segmentation, and, vice versa, the segmentation results are required for a good classification (e.g. using texture and shape parameters). After a short overview of segmentation methods, this contribution concentrates on segment growing methods for segmentation. Starting from a seed pixel, a segment is grown by adding neighbouring pixels as long as certain homogeneity criteria are fulfilled. The strategy for combined segmentation and classification for landcover mapping is based on: (i) the proper choice of seeds according to pixelwise classification, preventing e.g. the selection of mixed pixels as seed (which might lead to the formation of meaningless segments), (ii) land-cover-specific homogeneity criteria, causing segments to grow right to the boundaries of landcover patches, (iii) spatial subpixel analysis methods, reducing the influence of mixed pixels, and (iv) use of shape parameters of segments for classification refinement. The method is illustrated with examples of landcover mapping from Landsat TM images. 1. PROBLEM DEFINITION 1.1. Scope of this contribution This contribution deals with methodical problems of automated mapping of landcover from satellite images. The information needed for this essentially is of a biophysical nature and thus can be derived to a large extent from the remotely sensed images. In contrast to this, landuse mapping needs additional information on functional, socio-economic and cultural aspects, which often have to be taken from other sources (GIS). Landuse aspects are not considered here, although landcover maps may be used to derive landuse maps at a later stage. The restriction to optical satellite images implies that geometrical aspects of landcover identification, in particular 3D-effects, are neglected. The general discussion of the problem presented here can be adapted to various special applications, e.g. forest mapping. 1.2. Pixel-based versus segment-based classification Automated thematic mapping from remotely sensed images conventionally is performed by pixelwise statistical classifica tion. The main drawback of pixelwise classification is the fact that it neglects shape and context aspects of the image information, which are among the main clues for a human interpreter. In contrast to pixel-by-pixel techniques, image understanding (computer vision, knowledge-based) methods try to simulate human visual interpretation (Haralick and Shapiro, 1992, Gonzales and Woods, 1993). These techniques are based on the conceptual analysis model shown in Figure 1. The model follows the general approach of analytical science and technology of breaking complex reality down into indivi dual objects, identifying these objects, determining their attri butes and establishing relationships between the objects. Starting from a digital image, "objects" are delimited in the segmentation process. These "image objects" can conceptually be areas, lines, or points. In actuality, the image objects are sets of adjacent pixels having a meaning in the scene (i.e. the section of the real world shown in the image). The objects of an image are, in a second process, classified, i.e. each object is assigned to one category out of a set of pre defined categories, on the basis of the objects’ attributes and relationships to other objects. This classification can be seen as a process of "matching" (establishing correspondences) with prototypes (defining the categories) stored in a knowledge base. Classified image objects are termed “scene objects”. From a formal point of view, pixelwise classification may also be subsumed under this conceptual model. In this case, the segmentation process is left out, and the image objects to be classified are the individual pixels.

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