Wind turbine extraction from high spatial resolution remote sensing images based on saliency detection

Jingbo Chen; Anzhi Yue; Chengyi Wang; Qingqing Huang; Jiansheng Chen; Yu Meng; Dongxu He

doi:10.1117/1.JRS.12.016041

24 March 2018 Wind turbine extraction from high spatial resolution remote sensing images based on saliency detection

Jingbo Chen, Anzhi Yue, Chengyi Wang, Qingqing Huang, Jiansheng Chen, Yu Meng, Dongxu He

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 1, 016041 (March 2018). https://doi.org/10.1117/1.JRS.12.016041

Abstract

The wind turbine is a device that converts the wind’s kinetic energy into electrical power. Accurate and automatic extraction of wind turbine is instructive for government departments to plan wind power plant projects. A hybrid and practical framework based on saliency detection for wind turbine extraction, using Google Earth image at spatial resolution of 1 m, is proposed. It can be viewed as a two-phase procedure: coarsely detection and fine extraction. In the first stage, we introduced a frequency-tuned saliency detection approach for initially detecting the area of interest of the wind turbines. This method exploited features of color and luminance, was simple to implement, and was computationally efficient. Taking into account the complexity of remote sensing images, in the second stage, we proposed a fast method for fine-tuning results in frequency domain and then extracted wind turbines from these salient objects by removing the irrelevant salient areas according to the special properties of the wind turbines. Experiments demonstrated that our approach consistently obtains higher precision and better recall rates. Our method was also compared with other techniques from the literature and proves that it is more applicable and robust.

Citation Download Citation

Jingbo Chen, Anzhi Yue, Chengyi Wang, Qingqing Huang, Jiansheng Chen, Yu Meng, and Dongxu He "Wind turbine extraction from high spatial resolution remote sensing images based on saliency detection," Journal of Applied Remote Sensing 12(1), 016041 (24 March 2018). https://doi.org/10.1117/1.JRS.12.016041

Received: 19 October 2017; Accepted: 9 February 2018; Published: 24 March 2018

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