SVA-SSD: saliency visual attention single shot detector for building detection in low contrast high-resolution satellite images

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 tr...

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Autores principales:	Ahmed I. Shahin, Sultan Almotairi
Formato:	article
Lenguaje:	EN
Publicado:	PeerJ Inc. 2021
Materias:	Building detection Visual attention Spectral saliency features Aerial images Urban planning Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/94a750572614452e87bed67256c53060
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id	oai:doaj.org-article:94a750572614452e87bed67256c53060
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spelling	oai:doaj.org-article:94a750572614452e87bed67256c530602021-11-13T15:05:17ZSVA-SSD: saliency visual attention single shot detector for building detection in low contrast high-resolution satellite images10.7717/peerj-cs.7722376-5992https://doaj.org/article/94a750572614452e87bed67256c530602021-11-01T00:00:00Zhttps://peerj.com/articles/cs-772.pdfhttps://peerj.com/articles/cs-772/https://doaj.org/toc/2376-5992Building 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.Ahmed I. ShahinSultan AlmotairiPeerJ Inc.articleBuilding detectionVisual attentionSpectral saliency featuresAerial imagesUrban planningElectronic computers. Computer scienceQA75.5-76.95ENPeerJ Computer Science, Vol 7, p e772 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Building detection Visual attention Spectral saliency features Aerial images Urban planning Electronic computers. Computer science QA75.5-76.95
spellingShingle	Building detection Visual attention Spectral saliency features Aerial images Urban planning Electronic computers. Computer science QA75.5-76.95 Ahmed I. Shahin Sultan Almotairi SVA-SSD: saliency visual attention single shot detector for building detection in low contrast high-resolution satellite images
description	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.
format	article
author	Ahmed I. Shahin Sultan Almotairi
author_facet	Ahmed I. Shahin Sultan Almotairi
author_sort	Ahmed I. Shahin
title	SVA-SSD: saliency visual attention single shot detector for building detection in low contrast high-resolution satellite images
title_short	SVA-SSD: saliency visual attention single shot detector for building detection in low contrast high-resolution satellite images
title_full	SVA-SSD: saliency visual attention single shot detector for building detection in low contrast high-resolution satellite images
title_fullStr	SVA-SSD: saliency visual attention single shot detector for building detection in low contrast high-resolution satellite images
title_full_unstemmed	SVA-SSD: saliency visual attention single shot detector for building detection in low contrast high-resolution satellite images
title_sort	sva-ssd: saliency visual attention single shot detector for building detection in low contrast high-resolution satellite images
publisher	PeerJ Inc.
publishDate	2021
url	https://doaj.org/article/94a750572614452e87bed67256c53060
work_keys_str_mv	AT ahmedishahin svassdsaliencyvisualattentionsingleshotdetectorforbuildingdetectioninlowcontrasthighresolutionsatelliteimages AT sultanalmotairi svassdsaliencyvisualattentionsingleshotdetectorforbuildingdetectioninlowcontrasthighresolutionsatelliteimages
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SVA-SSD: saliency visual attention single shot detector for building detection in low contrast high-resolution satellite images

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