Building extraction from high resolution imagery based on multi-scale object oriented classification and probabilistic Hough transform

Liu, Z.J.; Wang, J.; Liu, W.P.

doi:10.1109/igarss.2005.1525421

Cited by 56 publications

(34 citation statements)

References 8 publications

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“…Others include ancillary data such as road maps [21]. In many cases, standard morphological filtering techniques, or even the Hough transform, are applied at the end of the process to generalize and smooth the shape of those objects classified as buildings [2,[33][34][35]]. Other different high-level vision techniques have been used to detect buildings, but generally offer less satisfactory results than those based on classification, being usually focused on solving uncommon or particular cases.…”

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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“…In this paper, we present our study of high accuracy land-cover mapping using OBIA with 1-meter resolution aerial photography at a county scale. We referred to the past experience in OBIA application in classifying high-resolution aerial photography at relatively smaller [6,35] and larger [3,9] scales.…”

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confidence: 99%

Object-Based Land-Cover Mapping with High Resolution Aerial Photography at a County Scale in Midwestern USA

Shao

2014

Remote Sensing

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There are growing demands for detailed and accurate land cover maps in land system research and planning. Macro-scale land cover maps normally cannot satisfy the studies that require detailed land cover maps at micro scales. In the meantime, applying conventional pixel-based classification methods in classifying high-resolution aerial imagery is ineffective to develop high accuracy land-cover maps, especially in spectrally heterogeneous and complicated urban areas. Here we present an object-based approach that identifies land-cover types from 1-meter resolution aerial orthophotography and a 5-foot DEM. Our study area is Tippecanoe County in the State of Indiana, USA, which covers about a 1300 km 2 land area. We used a countywide aerial photo mosaic and normalized digital elevation model as input datasets in this study. We utilized simple algorithms to minimize computation time while maintaining relatively high accuracy in land cover mapping at a county scale. The aerial photograph was pre-processed using principal component transformation to reduce its spectral dimensionality. Vegetation and non-vegetation were separated via masks determined by the Normalized Difference Vegetation Index. A combination of segmentation algorithms with lower calculation intensity was used to generate image objects that fulfill the characteristics selection requirements. A hierarchical image object network was formed based on the segmentation results and used to assist the image object delineation at different spatial scales. Finally, expert knowledge regarding spectral, contextual, and geometrical aspects was employed in OPEN ACCESSRemote Sens. 2014, 6 11373 image object identification. The resultant land cover map developed with this object-based image analysis has more information classes and higher accuracy than that derived with pixel-based classification methods.

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“…Optical satellite imagery, aerial images, RADAR imagery, LIDAR data, Digital Surface Model (DSM) and Digital Terrain Model (DTM) are mostly used data types in this kind of studies (Haala and Brenner 1999;Li et al 2014;Rottensteiner and Briese 2002;Sohn and Dowman 2007). Different methods are used for building extraction; Objectbased Image Analysis (OBIA), which allows using spectral, contextual, textural and geometric properties of image objects, is one of the preferred methods, especially when working with high-resolution satellite images (Li et al 2014;Liu et al 2005;Shrivastava and Rai 2015).…”

Section: Introductionmentioning

confidence: 99%

Object Based Building Extraction and Building Period Estimation From Unmanned Aerial Vehicle Data

Çömert

Kaplan

2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The aim of this study is to examine whether it is possible to estimate the building periods with respect to the building heights in the urban scale seismic performance assessment studies by using the building height retrieved from the unmanned aerial vehicle (UAV) data. For this purpose, a small area, which includes eight residential reinforced concrete buildings, was selected in Eskisehir (Turkey) city center. In this paper, the possibilities of obtaining the building heights that are used in the estimation of building periods from UAV based data, have been investigated. The investigations were carried out in 3 stages; (i) Building boundary extraction with Object Based Image Analysis (OBIA), (ii) height calculation for buildings of interest from nDSM and accuracy assessment with the terrestrial survey. (iii) Estimation of building period using height information. The average difference between the periods estimated according to the heights obtained from field measurements and from the UAV data is 2.86% and the maximum difference is 13.2%. Results of this study have shown that the building heights retrieved from the UAV data can be used in the building period estimation in the urban scale vulnerability assessments.

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Building extraction from high resolution imagery based on multi-scale object oriented classification and probabilistic Hough transform

Cited by 56 publications

References 8 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

Object-Based Land-Cover Mapping with High Resolution Aerial Photography at a County Scale in Midwestern USA

Object Based Building Extraction and Building Period Estimation From Unmanned Aerial Vehicle Data

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