@inproceedings{2543231e09dc4baab4072641e00798f6,
title = "Fast airplane detection with hierarchical structure in large scene remote sensing images at high spatial resolution",
abstract = "In order to detect airplane efficiently in large scene remote sensing images with high spatial resolution, a hierarchical detection framework is proposed according to the process of locating the target area in the downsampled image and then detecting the target in the original high-resolution image of that target region. First, we locate the airport in the downsampled original high-resolution image based on line features (midpoint coordinate and angle) clustering. A visual saliency model based on Itti model is then applied to detect airplanes candidates in the resulting airport region with the original high-resolution image. Finally, scale invariant feature transformation and support vector machine are used to classify the airplanes candidates. Experimental results indicate that the proposed method presents higher recall rate and detection speed. Meanwhile, the time cost of airport detection is much less than representative methods.",
keywords = "Airplane detection, Airport detection, Large scene image, Saliency map, Straight line clustering",
author = "Hao Chen and Jing Zhao and Tong Gao and Wen Chen",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE; 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 ; Conference date: 22-07-2018 Through 27-07-2018",
year = "2018",
month = oct,
day = "31",
doi = "10.1109/IGARSS.2018.8517609",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4846--4849",
booktitle = "2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings",
address = "United States",
}