Automated detection of buildings from single VHR multispectral images using shadow information and graph cuts

Ok, Ali Özgün

doi:10.1016/j.isprsjprs.2013.09.004

Cited by 201 publications

(141 citation statements)

References 83 publications

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“…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. Overall, image thresholding techniques are essential in the processes of object detection.…”

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%

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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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Section: Cs Dsmentioning

confidence: 99%

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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“…Figure 1 shows the available very high-resolution (VHR) satellite images captured several days after the Wenchuan earthquake in 2008. The lack of appropriate satellite imagery poses a considerable challenge for current rapid building mapping techniques that require little or no human intervention [5], because (1) multiple monocular VHR images are obtained from sensors with different spectral, spatial and radiometric characteristics; (2) diverse buildings are scattered throughout areas with different backgrounds, such as plains, mountainous regions, rural or urban areas; and (3) variable imaging environments, such as haze or cloud cover conditions. Currently, a number of studies have documented methods of extracting buildings from remote-sensing images using low-level features, such as edge/line segments [6][7][8][9], corners [10], spectra, texture [11,12] and morphological features (MFs) [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…Currently, a number of studies have documented methods of extracting buildings from remote-sensing images using low-level features, such as edge/line segments [6][7][8][9], corners [10], spectra, texture [11,12] and morphological features (MFs) [13,14]. One study [5] has conducted a thorough review of previous studies on building detection using single monocular remote-sensing images. In the remainder of this section, we will provide an overview of recent work combining multiple features to detect buildings.…”

Section: Introductionmentioning

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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“…In this section we briefly review relevant methods in the literature on building detection. Decades ago the initial endeavor for building detection was relying on grouping of low level image features such as edge/line segments and/or corners to form building hypotheses (Ok, 2013). For instance, a generic model of the shapes of building was adopted in (Huertas and Nevatia, 1988) and shadows cast by buildings were used to confirm building hypotheses and to estimate their height.…”

Section: Introductionmentioning

confidence: 99%

Building Detection Using Aerial Images and Digital Surface Models

Jia¹,

Cui

Reinartz

2017

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

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ABSTRACT:In this paper a method for building detection in aerial images based on variational inference of logistic regression is proposed. It consists of three steps. In order to characterize the appearances of buildings in aerial images, an effective bag-of-Words (BoW) method is applied for feature extraction in the first step. In the second step, a classifier of logistic regression is learned using these local features. The logistic regression can be trained using different methods. In this paper we adopt a fully Bayesian treatment for learning the classifier, which has a number of obvious advantages over other learning methods. Due to the presence of hyper prior in the probabilistic model of logistic regression, approximate inference methods have to be applied for prediction. In order to speed up the inference, a variational inference method based on mean field instead of stochastic approximation such as Markov Chain Monte Carlo is applied. After the prediction, a probabilistic map is obtained. In the third step, a fully connected conditional random field model is formulated and the probabilistic map is used as the data term in the model. A mean field inference is utilized in order to obtain a binary building mask. A benchmark data set consisting of aerial images and digital surfaced model (DSM) released by ISPRS for 2D semantic labeling is used for performance evaluation. The results demonstrate the effectiveness of the proposed method.

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Automated detection of buildings from single VHR multispectral images using shadow information and graph cuts

Cited by 201 publications

References 83 publications

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

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

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

Building Detection Using Aerial Images and Digital Surface Models

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