Using multi-level fusion of local features for land-use scene classification with high spatial resolution images in urban coastal zones

doi:10.1016/j.jag.2018.03.010

	Using multi-level fusion of local features for land-use scene classification with high spatial resolution images in urban coastal zones
	Lu, Chen 1,2; Yang, Xiaomei 1,3; Wang, Zhihua 1,2; Li, Zhi 4
刊名	INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
	2018-08-01
卷号	70 页码:1-12
关键词	Land-use scene classification Local features fusion Multi-level Urban coastal zones
ISSN号	0303-2434
DOI	10.1016/j.jag.2018.03.010
通讯作者	Yang, Xiaomei(yangxm@lreis.ac.cn)
英文摘要	Monitoring scene-level land-use in urban coastal zones has become a critical and challenging task, due to the rising risk of marine disasters and the greater number of scene classes such as harbors. Since street blocks are physical containers of different classes of land-use in urban zones, some scene classification methods based on high-spatial-resolution remote sensing images take blocks segmented by roads as classification units. However, these methods extract handcrafted low-level features from remote sensing images, limiting their ability to represent street blocks. To extract semantically meaningful representations of street blocks, the sparse auto-encoder (SAE) model was employed for local feature extraction in this paper and a multi-level method based on the fusion of local features was proposed for block-based land-use scene classification in urban coastal zones. First, convolved feature maps of street blocks were extracted by taking the hidden layer of the SAE as convolution kernels. Then, the local features were fused at three levels to generate more robust and discriminative representations of patches in convolved feature maps. The combination patterns and the absolute relationship of local features were captured at the first and second level, respectively. A convolution neural network was utilized to make the local features more discriminative to semantic information at the third level. Finally, the bag-of-visual-words model was employed to generate global features for street blocks. The proposed method was tested for Qingdao, China using Gaofen-2 (GF-2) satellite images and an overall accuracy of 83.80% was achieved in the study area. The classification results indicate that the proposed method in concert with GF-2 images has potential for accurately monitoring land-use scenes in urban coastal zones.
资助项目	National Key Research and Development Program of China[2016YFC1402003] ; National Science Foundation of China[41671436] ; National Science Foundation of China[41421001] ; Innovation Project of LREIS[088RAA01YA]
WOS关键词	LATENT DIRICHLET ALLOCATION ; REMOTE-SENSING IMAGERY ; SATELLITE IMAGES ; AUTO-ENCODER ; METRICS
WOS研究方向	Remote Sensing
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000434005000001
资助机构	National Key Research and Development Program of China ; National Science Foundation of China ; Innovation Project of LREIS
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/54716]
专题	中国科学院地理科学与资源研究所
通讯作者	Yang, Xiaomei
作者单位	1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, 11A Datun Rd, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China 4.Chinese Acad Sci, Xinjiang Inst Ecol & Geog, Urumqi 830011, Peoples R China
推荐引用方式 GB/T 7714	Lu, Chen,Yang, Xiaomei,Wang, Zhihua,et al. Using multi-level fusion of local features for land-use scene classification with high spatial resolution images in urban coastal zones[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2018,70:1-12.
APA	Lu, Chen,Yang, Xiaomei,Wang, Zhihua,&Li, Zhi.(2018).Using multi-level fusion of local features for land-use scene classification with high spatial resolution images in urban coastal zones.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,70,1-12.
MLA	Lu, Chen,et al."Using multi-level fusion of local features for land-use scene classification with high spatial resolution images in urban coastal zones".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 70(2018):1-12.