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Sensors 2016, 16(11), 1862; doi:10.3390/s16111862

Power Transmission Tower Series Extraction in PolSAR Image Based on Time-Frequency Analysis and A-Contrario Theory

School of Electronic Information, Wuhan University, Wuhan 430072, China
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Author to whom correspondence should be addressed.
Academic Editor: Jonathan Li
Received: 5 July 2016 / Revised: 10 October 2016 / Accepted: 1 November 2016 / Published: 5 November 2016
(This article belongs to the Section Remote Sensors)
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Abstract

Based on Time-Frequency (TF) analysis and a-contrario theory, this paper presents a new approach for extraction of linear arranged power transmission tower series in Polarimetric Synthetic Aperture Radar (PolSAR) images. Firstly, the PolSAR multidimensional information is analyzed using a linear TF decomposition approach. The stationarity of each pixel is assessed by testing the maximum likelihood ratio statistics of the coherency matrix. Then, based on the maximum likelihood log-ratio image, a Cell-Averaging Constant False Alarm Rate (CA-CFAR) detector with Weibull clutter background and a post-processing operator is used to detect point-like targets in the image. Finally, a searching approach based on a-contrario theory is applied to extract the linear arranged targets from detected point-like targets. The experimental results on three sets of PolSAR data verify the effectiveness of this approach. View Full-Text
Keywords: time-frequency analysis; a-contrario theory; polarimetric SAR; target extraction; cell-averaging constant false alarm rate time-frequency analysis; a-contrario theory; polarimetric SAR; target extraction; cell-averaging constant false alarm rate
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Peng, D.; Zhang, H.; Guo, W.; Yang, W. Power Transmission Tower Series Extraction in PolSAR Image Based on Time-Frequency Analysis and A-Contrario Theory. Sensors 2016, 16, 1862.

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