TY - GEN
T1 - A Systematic Scheme for Automatic Airplane Detection from High-Resolution Remote Sensing Images
AU - Zhao, Jiao
AU - Han, Jing
AU - Yao, Jian
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Airport and airplane are typical objects in remote sensing research field. However, there are rare methods to detect airport and airplane in a unit system. In this paper, we propose a systematic scheme for airport detection and airplane detection from high-resolution remote sensing images. The airport detection part is mainly based on the parallel line features of runway, containing six main stages: down-sampling, Frequency-Tuned (FT) saliency detection, Line Segment Detector (LSD) line detection, line growing, parallel lines detection and line clustering. The airplane detection part is mainly based on Circle Frequency Filter (CF-filter) and a Fast R-CNN deep learning model. Experimental results on 500 high-resolution remote sensing images acquired more than 95% accuracy, and the average detection time was about 14 s, which proved that the proposed system was effective and efficient.
AB - Airport and airplane are typical objects in remote sensing research field. However, there are rare methods to detect airport and airplane in a unit system. In this paper, we propose a systematic scheme for airport detection and airplane detection from high-resolution remote sensing images. The airport detection part is mainly based on the parallel line features of runway, containing six main stages: down-sampling, Frequency-Tuned (FT) saliency detection, Line Segment Detector (LSD) line detection, line growing, parallel lines detection and line clustering. The airplane detection part is mainly based on Circle Frequency Filter (CF-filter) and a Fast R-CNN deep learning model. Experimental results on 500 high-resolution remote sensing images acquired more than 95% accuracy, and the average detection time was about 14 s, which proved that the proposed system was effective and efficient.
KW - Airplane detection
KW - Circle Frequency Filter
KW - Deep learning
KW - High-resolution remote sensing images
UR - http://www.scopus.com/inward/record.url?scp=85048582120&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048582120&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-92753-4_36
DO - 10.1007/978-3-319-92753-4_36
M3 - Conference contribution
AN - SCOPUS:85048582120
SN - 9783319927527
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 465
EP - 478
BT - Image and Video Technology - PSIVT 2017 International Workshops, Revised Selected Papers
A2 - Satoh, Shin’ichi
PB - Springer-Verlag
T2 - 8th Pacific Rim Symposium on Image and Video Technology, PSIVT 2017
Y2 - 20 November 2017 through 24 November 2017
ER -