干旱区生态环境知识资源中心(Arid): Deep Learning for Effective Refugee Tent Extraction Near Syria-Jordan Border

Arid

DOI	10.1109/LGRS.2020.2999354
	Deep Learning for Effective Refugee Tent Extraction Near Syria-Jordan Border
	Lu, Yan; Koperski, Krzysztof; Kwan, Chiman; Li, Jiang
通讯作者	Li, J (corresponding author), Old Dominion Univ, Dept Elect & Comp Engn, Norfolk, VA 23529 USA.
来源期刊	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
ISSN	1545-598X
EISSN	1558-0571
出版年	2021
卷号	18 期号:8 页码:1342-1346
英文摘要	Rukban is a desert area crossing the border between Syria and Jordan, and thousands of Syrian refugees fled into this area since the Syrian civil war in 2014. In the past few years, the number of refugee shelters for the forcibly displaced Syrian refugees in this area has increased rapidly. Estimating the location and number of refugee tents has become a key factor to maintain the sustainability of the refugee shelter camps. Manually counting the shelters is labor-intensive and sometimes prohibitive given the large quantities. In addition, these shelters/tents are usually small in size, irregular in shape, and sparsely distributed in a very large area and could be easily missed by the traditional image-analysis techniques, making the image-based approaches also challenging. In this letter, we proposed a deep fully convolutional neural network (FCN) model to extract automatically the refugee shelters/tents in the worldview-2 (WV-2) satellite images. In addition, we transferred knowledge in the pretrained VGG-16 model to improve the detection accuracy and network training convergence. We compared the proposed approach with the traditional spectral angle mapper (SAM) method, deep convolutional neural network (CNN) models, and the mask Region-based CNN (R-CNN) model. The experimental results show that the FCN model improved the overall accuracy by 4.49%, 3.54%, and 0.88% compared with the CNNs, SAM, and mask R-CNN models, and improved the precision by 34.61%, 41.99%, and 11.87%, respectively.
英文关键词	Satellites Feature extraction Computational modeling Training Task analysis Image resolution Object detection Deep learning fully convolutional network spectral angle mapper (SAM) transfer learning
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000675210700012
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/363557
作者单位	[Lu, Yan] Old Dominion Univ, Dept Computat Modeling & Simulat Engn, Norfolk, VA 23529 USA; [Koperski, Krzysztof] Maxar, Westminster, CO 80234 USA; [Kwan, Chiman] Appl Res LLC, Rockville, MD 20850 USA; [Li, Jiang] Old Dominion Univ, Dept Elect & Comp Engn, Norfolk, VA 23529 USA
推荐引用方式 GB/T 7714	Lu, Yan,Koperski, Krzysztof,Kwan, Chiman,et al. Deep Learning for Effective Refugee Tent Extraction Near Syria-Jordan Border[J],2021,18(8):1342-1346.
APA	Lu, Yan,Koperski, Krzysztof,Kwan, Chiman,&Li, Jiang.(2021).Deep Learning for Effective Refugee Tent Extraction Near Syria-Jordan Border.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,18(8),1342-1346.
MLA	Lu, Yan,et al."Deep Learning for Effective Refugee Tent Extraction Near Syria-Jordan Border".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 18.8(2021):1342-1346.