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    Using texture to tackle the problem of scale in land-cover classification


    Corcoran, Padraig and Winstanley, Adam C. (2008) Using texture to tackle the problem of scale in land-cover classification. In: Object-Based Image Analysis. Lecture Notes in Geoinformation and Cartography (LNGC) . Springer, pp. 113-132. ISBN 9783540770589

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    Abstract

    Object Based Image Analysis (OBIA) is a form of remote sensing which attempts to model the ability of the human visual system (HVS) to interpret aerial imagery. We argue that in many of its current implementations, OBIA is not an accurate model of this system. Drawing from current theories in cognitive psychology, we propose a new conceptual model which we believe more accurately represents how the HVS performs aerial image interpretation. The first step in this conceptual model is the generation of image segmentation where each area of uniform visual properties is represented correctly. The goal of this work is to implement this first step. To achieve this we extract a novel complementary set of intensity and texture gradients which offer increased discrimination strength over existing competing gradient sets. These gradients are then fused using a strategy which accounts for spatial uncertainty in boundary localization. Finally segmentation is performed using the watershed segmentation algorithm. Results achieved are very accurate and outperform the popular Canny gradient operator.

    Item Type: Book Section
    Additional Information: Cite as: Corcoran P., Winstanley A. (2008) Using texture to tackle the problem of scale in land-cover classification. In: Blaschke T., Lang S., Hay G.J. (eds) Object-Based Image Analysis. Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg
    Keywords: visual perception; watershed segmentation; feature fusion; land-cover classification; scale; texture; intensity; segmentation;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG
    Item ID: 10431
    Identification Number: https://doi.org/10.1007/978-3-540-77058-9_6
    Depositing User: Dr. Adam Winstanley
    Date Deposited: 16 Jan 2019 16:17
    Publisher: Springer
    Refereed: Yes
    URI:

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