A Stochastic Framework for the Identification of Building Rooftops Using a Single Remote Sensing Image

Katartzis, Antonis; Sahli, Hichem

doi:10.1109/tgrs.2007.904953

Cited by 74 publications

(56 citation statements)

References 19 publications

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“…Previous research defined a series of rules that buildings should accomplish [6]. Similar approaches were used by [3,[7][8][9], who detected edges and analyzed their mutual relationships to define building existence hypothesis. Some authors proposed the use of transforms between image representation spaces, such as Fourier [5] or Hough [10,11].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data

et al. 2011

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Abstract:In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers to the minimum height to be considered as building, defined using the LiDAR data, and the other refers to the presence of vegetation, which is defined according to the spectral response. The other approach follows the standard scheme of object-based image classification: segmentation, feature extraction and selection, and classification, here performed using decision trees. In addition, the effect of the inclusion in the building detection process of contextual relations with the shadows is evaluated. Quality assessment is performed at two different levels: area and object. Area-level evaluates the building delineation performance, whereas object-level assesses the accuracy in the spatial location of individual buildings. The results obtained show a high efficiency of the evaluated methods for building detection techniques, in particular the thresholding-based approach, when the parameters are properly adjusted and adapted to the type of urban landscape considered.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data

et al. 2011

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“…Katartzis and Sahli used Markov random fields (MRFs) in a stochastic framework for the identification of building rooftops [20]. Benedek et al integrated several low-level features in a multi-temporal marked point process model to detect buildings and their possible changes [21].…”

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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“…A theory that has been gaining ground in feature extraction, such as in the extraction of buildings, is the probabilistic theory of Markov Random Field (MRF) (Krishnamachari and Chellappa, 1996;Katartzis and Sahli, 2008;Ferraioli, 2010;Galvanin and Dal Poz, 2012;Fernandes and Dal Poz, 2016). The great advantage of using the MRF model is that this formalism easily allows the characterization of contextual information.…”

Section: Introductionmentioning

confidence: 99%

BUILDING ROOF BOUNDARY EXTRACTION FROM LiDAR AND IMAGE DATA BASED ON MARKOV RANDOM FIELD

Póz

Fernandes

2017

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

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ABSTRACT:In this paper a method for automatic extraction of building roof boundaries is proposed, which combines LiDAR data and highresolution aerial images. The proposed method is based on three steps. In the first step aboveground objects are extracted from LiDAR data. Initially a filtering algorithm is used to process the original LiDAR data for getting ground and non-ground points. Then, a region-growing procedure and the convex hull algorithm are sequentially used to extract polylines that represent aboveground objects from the non-ground point cloud. The second step consists in extracting corresponding LiDAR-derived aboveground objects from a high-resolution aerial image. In order to avoid searching for the interest objects over the whole image, the LiDAR-derived aboveground objects' polylines are photogrammetrically projected onto the image space and rectangular bounding boxes (sub-images) that enclose projected polylines are generated. Each sub-image is processed for extracting the polyline that represents the interest aboveground object within the selected sub-image. Last step consists in identifying polylines that represent building roof boundaries. We use the Markov Random Field (MRF) model for modelling building roof characteristics and spatial configurations. Polylines that represent building roof boundaries are found by optimizing the resulting MRF energy function using the Genetic Algorithm. Experimental results are presented and discussed in this paper.

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A Stochastic Framework for the Identification of Building Rooftops Using a Single Remote Sensing Image

Cited by 74 publications

References 19 publications

Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data

Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data

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

BUILDING ROOF BOUNDARY EXTRACTION FROM LiDAR AND IMAGE DATA BASED ON MARKOV RANDOM FIELD

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