干旱区生态环境知识资源中心(Arid): Deep built-structure counting in satellite imagery using attention based re-weighting

Arid

DOI	10.1016/j.isprsjprs.2019.03.014
	Deep built-structure counting in satellite imagery using attention based re-weighting
	Shakeel, Anza; Sultani, Waqas; Ali, Mohsen
通讯作者	Ali, Mohsen
来源期刊	ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
ISSN	0924-2716
EISSN	1872-8235
出版年	2019
卷号	151 页码:313-321
英文摘要	In this paper, we attempt to address the challenging problem of counting built-structures in the satellite imagery. Building density is a more accurate estimate of the population density, urban area expansion and its impact on the environment, than the built-up area segmentation. However, building shape variances, overlapping boundaries, and variant densities make this a complex task. To tackle this difficult problem, we propose a deep learning based regression technique for counting built-structures in satellite imagery. Our proposed framework intelligently combines features from different regions of satellite image using attention based re-weighting techniques. Multiple parallel convolutional networks are designed to capture information at different granulates. These features are combined into the FusionNet which is trained to weigh features from different granularity differently, allowing us to predict a precise building count. To train and evaluate the proposed method, we put forward a new large-scale and challenging built-structure-count dataset. Our dataset is constructed by collecting satellite imagery from diverse geographical areas (planes, urban centers, deserts, etc.,) across the globe (Asia, Europe, North America, and Africa) and captures the wide density of built structures. Detailed experimental results and analysis validate the proposed technique. FusionNet has Mean Absolute Error of 3.65 and R-squared measure of 88% over the testing data. Finally, we perform the test on the 274.3 x 10(3) m(2) of the unseen region, with the error of 19 buildings off the 656 buildings in that area.
英文关键词	Land use Deep learning Regression Attention based re-weighting Building count Built-up area segmentation
类型	Article
语种	英语
国家	Pakistan
开放获取类型	Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000469306300023
WOS关键词	NEURAL-NETWORKS ; BUILDINGS ; CLASSIFICATION ; INVARIANT
WOS类目	Geography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Physical Geography ; Geology ; 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/2XILL650/216572
作者单位	Informat Technol Univ, Dept Comp Sci, Lahore, Pakistan
推荐引用方式 GB/T 7714	Shakeel, Anza,Sultani, Waqas,Ali, Mohsen. Deep built-structure counting in satellite imagery using attention based re-weighting[J],2019,151:313-321.
APA	Shakeel, Anza,Sultani, Waqas,&Ali, Mohsen.(2019).Deep built-structure counting in satellite imagery using attention based re-weighting.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,151,313-321.
MLA	Shakeel, Anza,et al."Deep built-structure counting in satellite imagery using attention based re-weighting".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 151(2019):313-321.