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

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2016
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Zingman, Igor
Saupe, Dietmar
Penatti, Otavio
Lambers, Karsten
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We develop an approach for detection of ruins of livestock enclosures 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 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 pre-trained deep convolutional neural networks (CNN). The deep CNN features, though learned from a very different type of images, provided the basic ability to capture the visual concept of the livestock enclosures. However, our handcrafted rectangularity-size features showed considerably higher performance.

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Object detection, incomplete rectangles, man-made structures, maximal cliques, rectangularity feature, deep CNN features
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VersionDatumZusammenfassung
2017-08-02 08:24:22
3*
2016-03-22 15:57:07
After a minor revision the article was accepted to IEEE Transactions on Geoscience and Remote Sensing
2015-12-11 10:41:51
* Ausgewählte Version