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Detection of Fragmented Rectangular Enclosures in Very-High-Resolution Remote Sensing Images

Detection of Fragmented Rectangular Enclosures in Very-High-Resolution Remote Sensing Images

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ZINGMAN, Igor, Dietmar SAUPE, Otavio A. B. PENATTI, Karsten LAMBERS, 2016. Detection of Fragmented Rectangular Enclosures in Very-High-Resolution Remote Sensing Images. In: IEEE Transactions on Geoscience and Remote Sensing. 54(8), pp. 4580-4593. ISSN 0196-2892. eISSN 1558-0644

@article{Zingman2016Detec-32382, title={Detection of Fragmented Rectangular Enclosures in Very-High-Resolution Remote Sensing Images}, year={2016}, doi={10.1109/TGRS.2016.2545919}, 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} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/32382"> <dc:language>eng</dc:language> <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> <dc:contributor>Zingman, Igor</dc:contributor> <dcterms:issued>2016</dcterms:issued> <dc:creator>Zingman, Igor</dc:creator> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-08-02T08:26:28Z</dcterms:available> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/32382"/> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-08-02T08:26:28Z</dc:date> <dc:creator>Penatti, Otavio A. B.</dc:creator> <dcterms:title>Detection of Fragmented Rectangular Enclosures in Very-High-Resolution Remote Sensing Images</dcterms:title> <dc:contributor>Penatti, Otavio A. B.</dc:contributor> <dc:creator>Saupe, Dietmar</dc:creator> <dc:contributor>Lambers, Karsten</dc:contributor> <dc:contributor>Saupe, Dietmar</dc:contributor> <dc:creator>Lambers, Karsten</dc:creator> </rdf:Description> </rdf:RDF>

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