Sharing and cooperation in geo-information technology

Aziz, T. Lukman

Bibliographic data

Monograph

Persistent identifier:: 856479470

Author:: Aziz, T. Lukman

Title:: Sharing and cooperation in geo-information technology

Sub title:: ISPRS Commission VI Symposium, April 15 - 17, 1999, Bandung, Indonesia

Scope:: 1 Online-Ressource (130 Seiten)

Year of publication:: 1999

Place of publication:: London

Publisher of the original:: RICS Books

Identifier (digital):: 856479470

Illustration:: Illustrationen, Diagramme

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:: WG VI/3: INTERNATIONAL COOPERATION AND TECHNOLOGY TRANSFER

Write comment:: Der Aufsatz "Promotion Of The General Understanding And Improvement Of Public Relations, [...] ist im Original nicht enthalten.

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: DISCRETE MATHEMATIC FOR SPATIAL DATA CLASSIFICATION AND UNDERSTANDING. Luigi Mussio, Rossella Nocera, Daniela poli

Document type:: Monograph

Structure type:: Chapter

76 2" d algorithm - Minimization of the width of a level structure When two level structures of equal depths are found, it is possible to merge them suitably, in order to get a new level structure of minimum width. To this aim, the nodes belonging to the same level in the two level structures have already a final destination. On the contrary, the nodes whose level are different should be definitively assigned to the level of one structure (between the two ones). The selected level is chosen taking into account, level by level, the structure whose local width is smaller. Notice that the new level structure could be unrooted. 3 rd algorithm - Numeration Node numeration goes on, level by level, starting from a root. In the following it assigns, in a set of nodes a smaller number to the nodes connected to the nodes, whose number is the smallest one, in a previous set of nodes. In case of ambiguity, the degree of the nodes is taken into account. 4 th algorithm - Dissection Nodes of a very high degree, of very long connections in a (hyper) spatial graph should be removed from the graph, so that the previous algorithms supply the expected results. In these cases, a preliminary dissection of the graph may be performed, if necessary dividing the graph in dissected parts. The ordering of these parts takes place thereafter, applying the above defined numeration and ordering algorithms. More sophisticated methodologies and procedures can be performed obviously, but their exposition is omitted for sake of brevity. 4. RELATIONAL STRATEGIES (BELLONE, ET AL„ 1996A) Relational strategies are based on relational data description techniques. A relational description is defined by a list of primitives and a list of relations. These lists are composed of some primitives, each described by attribute-value pairs. The lists represent the relational part of the data description. They provide the context information necessary for resolving the similarity problem. Applying principles of perceptual grouping to a high level of data structure, a description independent of the different viewpoints and high data reliability can be obtained. Graphs, composed of nodes that denote primitives and inter-nodal lines denoting relations, are used to represent relational data descriptions. Segmentation and matching are carried out on these graphs and are based on the criteria of seldomness and similarity. Notice that signal disturbances caused by various sources of noise during data acquisition have impact on the subsequent information extracting process. The labeling of nodes is performed so that every node ( or primitive ) of the graph is characterized by the number and type of relations associated to it. A numeric value (or label) is calculated according to rules able to represent the specific characteristic. Nodes with identical labels have the same relational characteristic. To obtain further distinctiveness, additional labels or sub-labels will be associated to each node. Once a label or sub-label distinction is obtained in this way and a value will be associated to the tuple of labels describing the node. Within the group of nodes, a sorting (in descending order) of the associated labels will then be performed and the labels will be identified. For each node associated with these labels, the algorithm will applied again considering now connected second-level nodes. It is difficult to obtain complete distinctiveness for all nodes of a graph by applying the procedure described above recursively, as repetitive elements are frequently found in real world environments. The algorithm is applied no further than the third level. Once the labeled graphs have been constructed for every set of data, the process if exact matching is relatively straightforward and can be performed in three steps. • For each set of data of the first pair, the appertaining graphs will be listed in descending order according to their complexity measures. • For every element (or graph) of one if the sorted list, the second list is searched for all elements with the same complexity measure. Among these element pairs, only those with the same number of nodes and the same sum of ranks over all nodes will be further analyzed. • For each of these graph pairs, a sorted list of every graph nodes will be produced in descending label order and for duplicate labels in descending sub-label order. Only graph pairs that provide a complete 1:1 identity between the rows of their node list will be further considered for analytical steps. Between the nodes of these graphs a precise correspondence has been found. All unmatched graphs from the above exact matching process will be analyzed in this step. For every graph of a first pair, the approximate position of the corresponding graph in the second set of data can be calculated by means of parameters obtained by some analytical steps of the pair based on points extracted from exactly matching graph nodes. Thus only a limited number of graphs in the second set of data has to be considered for matching, i.e. only those to be found in the second area of interest. The inexact matching process is an extension of the exact matching process and can be described as follows. • For each graph pair, a sorted list of nodes will be produced as described for the process of exact matching. • Each node pair of the sorted lists will be examined to identify pairs with identical labels and sub-labels denoting matched nodes. • For the remaining unmatched nodes in each list, the above described algorithm for obtaining further distinctiveness will be applied considering only those connected nodes within the related list that have already been successfully matched. • For these unmatched nodes, step 2 will then be performed again and, if additional matching could be found, step 3 will be repeated once more. Inexact matching generally yields multiple correspondence solutions of which only the very first one encountered has been considered for the present approach. 5. FORM DESCRIPTORS (CRIPPA, ET AL., 1994) Form descriptors furnish an analytical representation of data. There are different cases of geometric shape that can be studied by different form descriptors: • a line referred to an one-dimensional domain can be investigated by line interpolation.

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