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DOI: 10.5445/IR/1000087845
Veröffentlicht am 26.11.2018
DOI: 10.5194/isprs-archives-XLII-1-59-2018

On the categorization of high activity objects using differential attribute profiles

Boldt, M.; Thiele, A.; Schulz, K.; Hinz, S.

Change detection represents a broad field of research being on demand for different applications (e.g. disaster management and land use / land cover monitoring). Since the detection itself only delivers information about location and date of the change event, it is limited against approaches dealing with the category, type, or class of the change objects. In contrast to classification, categorization denotes a feature-based clustering of entities (here: change objects) without using any class catalogue information. Therefore, the extraction of suitable features has to be performed leading to a clear distinction of the resulting clusters. In previous work, a change analysis workflow has been accomplished, which comprises both the detection, the categorization, and the classification of so-called high activity change objects extracted from a TerraSAR-X time series dataset. With focus on the features used in this study, the morphological differential attribute profiles (DAPs) turned out to be very promising. It was shown, that the DAP were essential for the construction of the principal components. In this paper, this circumstance is c ... mehr

Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
Publikationstyp Zeitschriftenaufsatz
Jahr 2018
Sprache Englisch
Identifikator ISSN: 1682-1750
URN: urn:nbn:de:swb:90-878454
KITopen ID: 1000087845
Erschienen in The international archives of photogrammetry, remote sensing and spatial information sciences
Band 42
Heft 1
Seiten 59-64
Bemerkung zur Veröffentlichung 2018 ISPRS Technical Commission I Midterm Symposium on Innovative Sensing - From Sensors to Methods and Applications; Karlsruhe; Germany; 10 October 2018 through 12 October 2018. Ed.: M. Weinmann
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