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 6 INTEGRATION OF IMAGE ANALYSIS AND GIS

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: INVESTIGATION OF SYNERGY EFFECTS BETWEEN SATELLITE IMAGERY AND DIGITAL TOPOGRAPHIC DATABASES BY USING INTEGRATED KNOWLEDGE PROCESSING. Dietmar Kunz

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 landuse poor landuse with are two of of an scene and is new. of this spectral time Therefore, a model-driven top-down approach can be integrated into the common data-driven bottom-up process of satellite image analysis. Figure 1 shows the flowchart of the analysis process. Common satellite image analysis techniques are restricted to pixel-based classification and involve the use of only one feature (spectral signature) and interactive selection of training areas. With this new approach these restrictions are avoided. Image and topographic databases give a description of the scene from their respective perspectives. By using the a priori semantic information of the topographic objects in the map, an automated selection of training areas is performed. This is done by overlaying the image with the topographic objects. The geometric errors between the image and map objects are eliminated through the large amount of training areas and the use of a histogram analysis. The learning of typical features for the object classes is necessary for the later step of classification. In the following step involving the semantic modelling of the topographic objects, both symbolic scene descriptions are linked and an unambiguous scene description with disjoint objects is built. Knowledge based techniques are applied for the classification of the resulting geometric disjoint objects. The result of the classification process is a complete semantic scene description. This paper does not deal with the last step of updating the digital database, which involves its comparison with the semantic scene description. 2. KNOWLEDGE BASED FEATURE EXTRACTION AND SEGMENTATION The common features in satellite image analysis, i.e. the spectral signatures (mean values, standard deviations), have been proven to be insufficient for high quality results (Bähr and Vögtle, 1991; Vögtle and Schilling, 1995). Therefore, these features have to be extended to spectral as well as non spectral parameters, which can contribute to an improved distinction between the defined object classes. Thus, geometrical and structural features are taken into account (Table 1): Spectral features Spectral Signature Texture Non-spectral features Structure Size Shape/Contour Neighbourhood Relations Table 1. Selected features for image analysis. The automated extraction of the above defined features is based on the a priori knowledge represented in the topographic database ATKIS-DLM200, which offers both a (possibly not up-to-date) geometric and semantic description of those landuse objects to be extracted from satellite images. In contrast to the commonly used method, where a human operator interactively has to define some representative training areas based on his experience and intuition, now all DLM-objects of the same class within the geocoded satellite image can be used as training areas without human interaction. Therefore, a very large sample is taken and a robust estimation of the defined features is performed to exclude disturbances caused by errors in the topographic database or in the image information, e.g. out-of-date status of some polygons (contour lines), digitizing errors or errors in the geometric correction of the satellite image. For a robust estimation, it is assumed that for each class in the image at least more than 50% of the underlying DLM object area belongs to the DLM class, a condition which is fulfilled in most cases. The feature extraction process in this project contains a hierarchical concept. The spectral characteristics of the objects is still one of the most important features in satellite image information. To get a robust estimation of the spectral signatures of each object class, only the representative reflectance values for this class are extracted. For relatively homogeneous objects, like 'water' or 'forest', statistical methods have been proven to be sufficient, e.g. histogram analysis (extraction of the standard deviation) or a median estimation. Inhomogeneous objects, like 'settlement areas', can not be treated in this way. Typically, these areas contain a strong mixture of different (sub-)objects (man-made objects, meadows, gardens, trees, water areas etc.), and therefore, a wide range of reflectance values. Nevertheless, the accumulation of vegetation-free pixels caused by man made objects (e.g. buildings and traffic areas) can be seen as representative for 'settlement'. With respect to this knowledge, vegetation-free pixels can be extracted, by means of the NDVI (Normalized Difference Vegetation Index): NDVI = (IR-R) (ir + r) IR ...reflectance values in near infrared R ...reflectance values in visible red In Fig. 2, the NDVI for different topographic classes is shown. Fig. 2. Normalized Difference Vegetation Index (NDVI).

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