A novel airport extraction model based on saliency region detection for high spatial resolution remote sensing images

Wen Lv; Libao Zhang; Yongchun Zhu

doi:10.1117/12.2269950

26 June 2017 A novel airport extraction model based on saliency region detection for high spatial resolution remote sensing images

Wen Lv, Libao Zhang, Yongchun Zhu

Author Affiliations +

Proceedings Volume 10334, Automated Visual Inspection and Machine Vision II; 103340I (2017) https://doi.org/10.1117/12.2269950
Event: SPIE Optical Metrology, 2017, Munich, Germany

Abstract

The airport is one of the most crucial traffic facilities in military and civil fields. Automatic airport extraction in high spatial resolution remote sensing images has many applications such as regional planning and military reconnaissance. Traditional airport extraction strategies usually base on prior knowledge and locate the airport target by template matching and classification, which will cause high computation complexity and large costs of computing resources for high spatial resolution remote sensing images. In this paper, we propose a novel automatic airport extraction model based on saliency region detection, airport runway extraction and adaptive threshold segmentation. In saliency region detection, we choose frequency-tuned (FT) model for computing airport saliency using low level features of color and luminance that is easy and fast to implement and can provide full-resolution saliency maps. In airport runway extraction, Hough transform is adopted to count the number of parallel line segments. In adaptive threshold segmentation, the Otsu threshold segmentation algorithm is proposed to obtain more accurate airport regions. The experimental results demonstrate that the proposed model outperforms existing saliency analysis models and shows good performance in the extraction of the airport.

Citation Download Citation

Wen Lv, Libao Zhang, and Yongchun Zhu "A novel airport extraction model based on saliency region detection for high spatial resolution remote sensing images", Proc. SPIE 10334, Automated Visual Inspection and Machine Vision II, 103340I (26 June 2017); https://doi.org/10.1117/12.2269950

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