干旱区生态环境知识资源中心(Arid): SVA-SSD: saliency visual attention single shot detector for building detection in low contrast high-resolution satellite images

Arid

DOI	10.7717/peerj-cs.772
	SVA-SSD: saliency visual attention single shot detector for building detection in low contrast high-resolution satellite images
	Shahin, Ahmed, I; Almotairi, Sultan
通讯作者	Shahin, AI (corresponding author), Majmaah Univ, Community Coll, Dept Nat & Appl Sci, Al Majmaah, Saudi Arabia.
来源期刊	PEERJ COMPUTER SCIENCE
EISSN	2376-5992
出版年	2021
卷号	7
英文摘要	Building detection in high-resolution satellite images has received great attention, as it is important to increase the accuracy of urban planning. The building boundary detection in the desert environment is a real challenge due to the nature of low contrast images in the desert environment. The traditional computer vision algorithms for building boundary detection lack scalability, robustness, and accuracy. On the other hand, deep learning detection algorithms have not been applied to such low contrast satellite images. So, there is a real need to employ deep learning algorithms for building detection tasks in low contrast high-resolution images. In this paper, we propose a novel building detection method based on a single-shot multi-box (SSD) detector. We develop the state-of-the-art SSD detection algorithm based on three approaches. First, we propose data-augmentation techniques to overcome the low contrast images' appearance. Second, we develop the SSD backbone using a novel saliency visual attention mechanism. Moreover, we investigate several pre-trained networks performance and several fusion functions to increase the performance of the SSD backbone. The third approach is based on optimizing the anchor-boxes sizes which are used in the detection stage to increase the performance of the SSD head. During our experiments, we have prepared a new dataset for buildings inside Riyadh City, Saudi Arabia that consists of 3878 buildings. We have compared our proposed approach vs other approaches in the literature. The proposed system has achieved the highest average precision, recall, F1-score, and IOU performance. Our proposed method has achieved a fast average prediction time with the lowest variance for our testing set. Our experimental results are very promising and can be generalized to other object detection tasks in low contrast images.
英文关键词	Building detection Visual attention Spectral saliency features Aerial images Urban planning
类型	Article
语种	英语
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:000719586700001
WOS关键词	CLASSIFICATION ; FEATURES
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Computer Science, Theory & Methods
WOS研究方向	Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/373855
作者单位	[Shahin, Ahmed, I; Almotairi, Sultan] Majmaah Univ, Community Coll, Dept Nat & Appl Sci, Al Majmaah, Saudi Arabia
推荐引用方式 GB/T 7714	Shahin, Ahmed, I,Almotairi, Sultan. SVA-SSD: saliency visual attention single shot detector for building detection in low contrast high-resolution satellite images[J],2021,7.
APA	Shahin, Ahmed, I,&Almotairi, Sultan.(2021).SVA-SSD: saliency visual attention single shot detector for building detection in low contrast high-resolution satellite images.PEERJ COMPUTER SCIENCE,7.
MLA	Shahin, Ahmed, I,et al."SVA-SSD: saliency visual attention single shot detector for building detection in low contrast high-resolution satellite images".PEERJ COMPUTER SCIENCE 7(2021).