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

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...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2016-08, Vol.54 (8), p.4580-4593
Main Authors: Zingman, Igor, Saupe, Dietmar, Penatti, Otavio A. B., Lambers, Karsten
Format: Article
Language:English
Subjects:
Buildings
Contours
Deep features
Detectors
Enclosures
Feature extraction
Fragmentation
Image detection
Image edge detection
Image resolution
incomplete rectangles
Livestock
man-made structures
maximal cliques
Neural networks
object detection
rectangularity feature
Remote sensing
Shape
Walls
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Summary: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.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2016.2545919