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      Automatic mapping of linear woody vegetation features in agricultural landscapes using very high resolution imagery

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      Author
      Aksoy, S.
      Akçay H. G.
      Wassenaar, T.
      Date
      2010
      Source Title
      IEEE Transactions on Geoscience and Remote Sensing
      Print ISSN
      0196-2892
      Publisher
      Institute of Electrical and Electronics Engineers
      Volume
      48
      Issue
      1
      Pages
      511 - 522
      Language
      English
      Type
      Article
      Item Usage Stats
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      Abstract
      Automatic mapping and monitoring of agricultural landscapes using remotely sensed imagery has been an important research problem. This paper describes our work on developing automatic methods for the detection of target landscape features in very high spatial resolution images. The target objects of interest consist of linear strips of woody vegetation that include hedgerows and riparian vegetation that are important elements of the landscape ecology and biodiversity. The proposed framework exploits the spectral, textural, and shape properties of objects using hierarchical feature extraction and decision-making steps. First, a multifeature and multiscale strategy is used to be able to cover different characteristics of these objects in a wide range of landscapes. Discriminant functions trained on combinations of spectral and textural features are used to select the pixels that may belong to candidate objects. Then, a shape analysis step employs morphological top-hat transforms to locate the woody vegetation areas that fall within the width limits of an acceptable object, and a skeletonization and iterative least-squares fitting procedure quantifies the linearity of the objects using the uniformity of the estimated radii along the skeleton points. Extensive experiments using QuickBird imagery from three European Union member states show that the proposed algorithms provide good localization of the target objects in a wide range of landscapes with very different characteristics. © 2009 IEEE.
      Keywords
      Linear object detection
      Multiscale texture analysis
      Object-based performance evaluation
      Shape analysis
      Permalink
      http://hdl.handle.net/11693/22466
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/TGRS.2009.2027702
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