Automated Detection of Arbitrarily Shaped Buildings in Complex Environments From Monocular VHR Optical Satellite Imagery

Ok, Ali Özgün; Şenaras, Çağlar; Yüksel, Baríş

doi:10.1109/tgrs.2012.2207123

Cited by 170 publications

(111 citation statements)

References 44 publications

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“…In our experiments, three well-known quality measures (recall, precision and F-value) were used to evaluate the performance of the proposed method as applied in a previous study [30], which were calculated as follows:…”

Section: Evaluation Methodsmentioning

confidence: 99%

Automated Detection of Buildings from Heterogeneous VHR Satellite Images for Rapid Response to Natural Disasters

Tang

Huang

et al. 2017

Remote Sensing

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Abstract:In this paper, we present a novel approach for automatically detecting buildings from multiple heterogeneous and uncalibrated very high-resolution (VHR) satellite images for a rapid response to natural disasters. In the proposed method, a simple and efficient visual attention method is first used to extract built-up area candidates (BACs) from each multispectral (MS) satellite image. After this, morphological building indices (MBIs) are extracted from all the masked panchromatic (PAN) and MS images with BACs to characterize the structural features of buildings. Finally, buildings are automatically detected in a hierarchical probabilistic model by fusing the MBI and masked PAN images. The experimental results show that the proposed method is comparable to supervised classification methods in terms of recall, precision and F-value.

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Section: Evaluation Methodsmentioning

confidence: 99%

Automated Detection of Buildings from Heterogeneous VHR Satellite Images for Rapid Response to Natural Disasters

Tang

Huang

et al. 2017

Remote Sensing

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“…This is so especially in cases where the shadow regions are used as evidence of the structure and location of the elevated objects within the urban area, such as buildings. The same approach has been used by Ok et al (2013) to detect shadow regions for identifying the regions recognised as buildings and roads, implemented on a set of Quickbird and Geoeye-1 VHR images. A post-processing shadow mask, which included a constrained region growing process on detected shadow regions and probabilistic landscape approach (Ok, 2013a), was achieved using three different thresholds: intensity, ratio and height in order to obtain a regular shape identical to reality and eliminate the unwanted shadow areas.…”

Section: Cs Dsmentioning

confidence: 99%

“…Furthermore, because shadows cover a considerable portion of an image, they play a supporting role on automated analysis. In this context, the shadow presence in single VHR multispectral images has been exploited as strong evidence of the existence of a different building structure, such as buildings detection (Ok, 2013a), arbitrarily shaped buildings in complex environments (Ok et al, 2013), the extraction of above ground circular structures (Ok, 2014), and automated extraction of buildings and roads (Ok, 2013b). Shadows are an important cue for information not only about man-made structures but also about supporting urban sustainability.…”

Section: The Importance Of Shadow Detection In Urban Environmentsmentioning

confidence: 99%

Shadow Detection From Very High Resoluton Satellite Image Using Grabcut Segmentation and Ratio-Band Algorithms

Kadhim

Mourshed

Bray

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Very-High-Resolution (VHR) satellite imagery is a powerful source of data for detecting and extracting information about urban constructions. Shadow in the VHR satellite imageries provides vital information on urban construction forms, illumination direction, and the spatial distribution of the objects that can help to further understanding of the built environment. However, to extract shadows, the automated detection of shadows from images must be accurate. This paper reviews current automatic approaches that have been used for shadow detection from VHR satellite images and comprises two main parts. In the first part, shadow concepts are presented in terms of shadow appearance in the VHR satellite imageries, current shadow detection methods, and the usefulness of shadow detection in urban environments. In the second part, we adopted two approaches which are considered current state-of-theart shadow detection, and segmentation algorithms using WorldView-3 and Quickbird images. In the first approach, the ratios between the NIR and visible bands were computed on a pixel-by-pixel basis, which allows for disambiguation between shadows and dark objects. To obtain an accurate shadow candidate map, we further refine the shadow map after applying the ratio algorithm on the Quickbird image. The second selected approach is the GrabCut segmentation approach for examining its performance in detecting the shadow regions of urban objects using the true colour image from WorldView-3. Further refinement was applied to attain a segmented shadow map. Although the detection of shadow regions is a very difficult task when they are derived from a VHR satellite image that comprises a visible spectrum range (RGB true colour), the results demonstrate that the detection of shadow regions in the WorldView-3 image is a reasonable separation from other objects by applying the GrabCut algorithm. In addition, the derived shadow map from the Quickbird image indicates significant performance of the ratio algorithm. The differences in the characteristics of the two satellite imageries in terms of spatial and spectral resolution can play an important role in the estimation and detection of the shadow of urban objects.

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“…(Ok et al, 2013) proposed the Grab-Cut algorithm for building detection. The algorithm contains a two level graph partitioning framework and increasing the performance of his previous proposed model (Ok et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Learning Oriented Region-based Convolutional Neural Networks for Building Detection in Satellite Remote Sensing Images

Chen

Gong

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The automated building detection in aerial images is a fundamental problem encountered in aerial and satellite images analysis. Recently, thanks to the advances in feature descriptions, Region-based CNN model (R-CNN) for object detection is receiving an increasing attention. Despite the excellent performance in object detection, it is problematic to directly leverage the features of R-CNN model for building detection in single aerial image. As we know, the single aerial image is in vertical view and the buildings possess significant directional feature. However, in R-CNN model, direction of the building is ignored and the detection results are represented by horizontal rectangles. For this reason, the detection results with horizontal rectangle cannot describe the building precisely. To address this problem, in this paper, we proposed a novel model with a key feature related to orientation, namely, Oriented R-CNN (OR-CNN). Our contributions are mainly in the following two aspects: 1) Introducing a new oriented layer network for detecting the rotation angle of building on the basis of the successful VGG-net R-CNN model; 2) the oriented rectangle is proposed to leverage the powerful R-CNN for remote-sensing building detection. In experiments, we establish a complete and bran-new data set for training our oriented R-CNN model and comprehensively evaluate the proposed method on a publicly available building detection data set. We demonstrate State-of-the-art results compared with the previous baseline methods.

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Automated Detection of Arbitrarily Shaped Buildings in Complex Environments From Monocular VHR Optical Satellite Imagery

Cited by 170 publications

References 44 publications

Automated Detection of Buildings from Heterogeneous VHR Satellite Images for Rapid Response to Natural Disasters

Automated Detection of Buildings from Heterogeneous VHR Satellite Images for Rapid Response to Natural Disasters

Shadow Detection From Very High Resoluton Satellite Image Using Grabcut Segmentation and Ratio-Band Algorithms

Learning Oriented Region-based Convolutional Neural Networks for Building Detection in Satellite Remote Sensing Images

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