@article{Zingman2016Detec-32382,
  year={2016},
  doi={10.1109/TGRS.2016.2545919},
  title={Detection of Fragmented Rectangular Enclosures in Very-High-Resolution Remote Sensing Images},
  number={8},
  volume={54},
  issn={0196-2892},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  pages={4580--4593},
  author={Zingman, Igor and Saupe, Dietmar and Penatti, Otavio A. B. and Lambers, Karsten}
}

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    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-08-02T08:26:28Z</dc:date>
    <dc:creator>Saupe, Dietmar</dc:creator>
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    <dc:contributor>Penatti, Otavio A. B.</dc:contributor>
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    <dc:contributor>Saupe, Dietmar</dc:contributor>
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    <dcterms:abstract xml:lang="eng">We develop an approach for the detection of ruins of livestock enclosures (LEs) in alpine areas captured by high-resolution remotely sensed images. These structures are usually of approximately rectangular shape and appear in images as faint fragmented contours in complex background. We address this problem by introducing a rectangularity feature that quantifies the degree of alignment of an optimal subset of extracted linear segments with a contour of rectangular shape. The rectangularity feature has high values not only for perfectly regular enclosures but also for ruined ones with distorted angles, fragmented walls, or even a completely missing wall. Furthermore, it has a zero value for spurious structures with less than three sides of a perceivable rectangle. We show how the detection performance can be improved by learning a linear combination of the rectangularity and size features from just a few available representative examples and a large number of negatives. Our approach allowed detection of enclosures in the Silvretta Alps that were previously unknown. A comparative performance analysis is provided. Among other features, our comparison includes the state-of-the-art features that were generated by pretrained deep convolutional neural networks (CNNs). The deep CNN features, although learned from a very different type of images, provided the basic ability to capture the visual concept of the LEs. However, our handcrafted rectangularity-size features showed considerably higher performance.</dcterms:abstract>
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    <dc:contributor>Lambers, Karsten</dc:contributor>
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    <dcterms:title>Detection of Fragmented Rectangular Enclosures in Very-High-Resolution Remote Sensing Images</dcterms:title>
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