Building detection in SAR imagery

Steinbach, Ryan Matthew; Koch, Mark W.; Moya, Mary M.; Goold, Jeremy

doi:10.1117/12.2177037

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Some studies utilised shadow for target detection, especially MTD (moving target detection). For example, Steinbach et al located buildings with shadows as aid information [4] based on the phenomenon that a moving target will be displaced while its corresponding shadow appears at the correct position in SAR images, Xu and Jahangir et al proposed a method to conduct MTD by firstly finding the shadow and then applying a spatial matching algorithm [2,5,6]. Moreover, some scholars also focus on applying shadows to feature extraction or ATR (automatic target recognition).…”

Section: Introductionmentioning

confidence: 99%

A feed‐forward framework integrating saliency and geometry discrimination for shadow detection in SAR images

Zou

et al. 2021

IET Radar Sonar & Navi

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Shadow has been increasingly a kind of significant aid information for object extraction and scene interpretation in synthetic aperture radar images, which makes SAR shadow detection an important issue. In this paper, we propose a feed‐forward framework integrating saliency and geometry discrimination for shadow detection in SAR images. We firstly develop a global contrast based shadow saliency model to extract suspected shadows. Considering that such suspected regions mostly contain some non‐shadow areas, a discrimination strategy based on geometric relationships between objects and shadows is designed to remove falsely detected areas. Then the remaining regions become the final shadow detection results. Several experiments are carried out on images from two real datasets, Moving and Stationary Target Acquisition Recognition and MiniSAR, to evaluate the performance of our method. From the perspectives of three commonly used metrics, the proposed algorithm comprehensively outperforms two other classic methods, presenting reliable shadow detection ability. Moreover, the detection results of the two classic methods are significantly enhanced in controlling false alarms after the discrimination module is introduced. The results demonstrate that our algorithm is practically applicable to shadow detection in SAR images, and the discrimination strategy can be flexibly extended for related tasks.

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Section: Introductionmentioning

confidence: 99%

A feed‐forward framework integrating saliency and geometry discrimination for shadow detection in SAR images

Zou

et al. 2021

IET Radar Sonar & Navi

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Identification of Maya ruins covered by jungle using Sentinel-1

Michenot,

Hinostroza,

Guinvarc’h

et al. 2024

Sci Rep

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Archaeologists commonly use airborne LIDAR technology to produce 3D models of structures, even when obscured by a forest canopy. However, this technology has a high cost, both from the plane itself and from the processing of the LIDAR point cloud. Furthermore, this technique can only be used over limited regions. This paper proposes a technique that uses SAR satellite imagery to identify man-made structures hidden by a forest canopy. To do so, we exploit the Ascending and Descending passes of Sentinel-1 so that we obtain two images of the candidate site but from different sight directions. Because of cardinal effects, a large enough building will sign differently from the comparatively isotropic forest canopy it is obscured by. Practically, the technique is based on the ratio of backscattered intensity from these two illumination angles and is well adapted for large areas. The advantages and shortcomings are discussed for the specific case of Sentinel-1 SAR images over two Maya archaeological sites in Central America. Our analysis shows that SAR satellite imagery might provide a free, global-scale way of preselecting sites with large or tall structures to complement LIDAR technology.

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Shadow Detection in SAR Images: An OTSU- and CFAR-Based Method

Sun

et al. 2020

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

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Building detection in SAR imagery

Cited by 3 publications

References 17 publications

A feed‐forward framework integrating saliency and geometry discrimination for shadow detection in SAR images

A feed‐forward framework integrating saliency and geometry discrimination for shadow detection in SAR images

Identification of Maya ruins covered by jungle using Sentinel-1

Shadow Detection in SAR Images: An OTSU- and CFAR-Based Method

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