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

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

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ZINGMAN, Igor, Dietmar SAUPE, Otavio PENATTI, Karsten LAMBERS, 2015. Detection of Fragmented Rectangular Enclosures in Very-High-Resolution Remote Sensing Images

@article{Zingman2015Detec-32382.1, title={Detection of Fragmented Rectangular Enclosures in Very-High-Resolution Remote Sensing Images}, year={2015}, author={Zingman, Igor and Saupe, Dietmar and Penatti, Otavio and Lambers, Karsten} }

2015-12-11T10:41:51Z Detection of Fragmented Rectangular Enclosures in Very-High-Resolution Remote Sensing Images Zingman, Igor 2015 Penatti, Otavio Penatti, Otavio Lambers, Karsten Zingman, Igor Lambers, Karsten eng Saupe, Dietmar 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.<br />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.<br />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. terms-of-use 2015-12-11T10:41:51Z Saupe, Dietmar

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