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Wind turbine segmentation performing kNN-clustering on superpixel segmentations
Author(s): Torben Möller; Daniel Langenkämper; Tim Nattkemper
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Paper Abstract

A growing number of offshore wind farms have been deployed in recent years, comprising up to hundreds of wind turbines. The high number of wind turbines and the effort to reach an offshore wind turbine motivate the use of unmanned aerial vehicles (UAV) or drones for remote visual inspection. The images taken by the drones need to be inspected to detect and classify various kinds of damages. To leverage the effort for the detection and classification of damages, it is a good practice to segment the wind turbines from the background in a first step. In this paper, we compare the speed and segmentation accuracy of a k-Nearest-Neighbor classifier when applied to pixels or superpixels. Pixel-by-pixel classification is compared to superpixel classification using two different kinds of superpixel representations: Superpixels gained by Single Linear Iterative Clustering (SLIC) and Boundary Aware Superpixel Segmentation (BASS). Our results clearly show that superpixel based segmentation increases the segmentation speed by a relevant factor while the segmentation accuracy is at least on the same level or even improves in one dataset.

Paper Details

Date Published: 7 October 2019
PDF: 7 pages
Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 1115524 (7 October 2019); doi: 10.1117/12.2532764
Show Author Affiliations
Torben Möller, Univ. Bielefeld (Germany)
Daniel Langenkämper, Univ. Bielefeld (Germany)
Tim Nattkemper, Univ. Bielefeld (Germany)


Published in SPIE Proceedings Vol. 11155:
Image and Signal Processing for Remote Sensing XXV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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