Enter records

ID: 59557
Title: Soil respiration mapped by exclusively use of MODIS data for forest landscapes of Saskatchewan, Canada.
Author: Chaoyang Wu, David Gaumount-Guay, T. Andrew Black, Rachhpal S. Jassal, Shiguang Xu, Jing M. Chen, Alemu Gonsamo.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 94. 80-90 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Soil respiration, Forest, Soil temperature, Remote sensing, MODIS, NDVI, Land surface temperature.
Abstract: Soil respiration (Rs) is of great importance to the global carbon balance. Remote sensing of Rs is challenging because of (1) the lack of long-term Rs data for model development and (2) limited knowledge of using satellite-based products to estimate Rs. Using 8-years (2002-2009) of continuous Rs measurements with non-steady-state automated chamber systems at a Canadian boreal black spruce stand (SK-OBS), we found that Rs was strongly correlated with the product of the normalized difference vegetation index (NDVI) and the nighttime land surface temperature (LSTn) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The coefficients of the linear regression equation of this correlation between Rs and NDVI x LSTn could be further calibrated using the MODIS leaf area index (LAI) product, resulting in an algorithm that is driven solely by remote sensing observations. Modeled Rs closely tracked the seasonal patterns of measured Rs and explained 74-92% of the variance in Rs with a root mean square error (RMSE) less than 1.0 g C/m?/d. Further validation of the model from SK-OBS site at another two independent sites (SK-OA and SK-OJP, old aspen and old jack pine, respectively) showed that the algorithm can produce good estimates of Rs with an overall R? of 0.78 (p<0.001) for data of these two sites. Consequently, we mapped Rs of forest landscapes of Saskatchewan using entirely MODIS observations for 2003 and spatial and temporal patterns of Rs were well modeled. These results point to a strong relationship between the soil respiratory process and canopy photosynthesis as indicated from the greenness index (i.e., NDVI), thereby implying the potential of remote sensing data for detecting variations in Rs. A combination of both biological and environmental variables estimated from remote sensing in this analysis may be valuable in future investigations of spatial and temporal characteristics of Rs.
Location: TE 12 New Biology Building
Literature cited 1: Albergel, C., de Rosnay, P., Gruhier, C., Munoz-Sabater, J., Hasenauer, S., Isaksen, L., Kerr, Y., Wagner, W., 2012. Evaluation of remotely sensed and modeled soil moisture products using global ground-based in situ observations. Remote Sens. Environ. 118, 215-226. Bahn, M., Reichstein. M., Davidson. E.A., Grunzweig, J., Jung, M., Carbone, M.S., et al. 2010. Soil respiration at mean annual temperature predicts annual total across vegetation types and biomes. Biogeosciences 7, 2147-2157.
Literature cited 2: Barr, A.G., Black, T.A., and Hogg. E.H., Kljun, N., Morgenstern, K., Nesic. Z., 2004. Interannual variability in the leaf area index of a boreal aspen-hazelnut forest in relation to net ecosystem production. Agr. Forest Metereol.126 (3-4), 237-255. Bisbee. K.E., Gower, S.T. Norman, J.M., Nordheim, E.V., 2001. Environmental controls on ground cover species composition and productivity in a boreal black spruce forest. Oecologia 129 (2), 261-270.

ID: 59556
Title: Eigen-feature analysis of weighted covariance matrices for LiDAR point cloud classification.
Author: Chao-Hung Lin, Jyun-Yuan Chen, Po-Lin Su, Chung-Hao Chen
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 94. 70-79 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Point cloud classification, Weighted covariance matrix, Eigen-feature.
Abstract: The features used in the separation of different objects are important for successful point cloud classification. Eigen-features from a covariance matrix of a point set with the sample mean are commonly used geometric features that can describe the local geometric characteristics of a point cloud and indicate whether the local geometry is linear, planar, or spherical. However, eigen-features calculated by the principal component analysis of a covariance matrix are sensitive to LiDAR data with inherent noise and incomplete shapes because of the non-robust statistical analysis. To obtain reliable eigen-features from LiDAR data and to improve classification accuracy, we introduce a method of analyzing local geometric characteristics of a point cloud by using a weighted covariance matrix with a geometric median. Each point is assigned a weight to represent its spatial contribution in the weighted principal component analysis and to estimate the geometric median which can be regarded as a localized center of a shape. In the experiments, qualitative and quantitative analyses on airborne LiDAR data and simulated point clouds show a clear improvement of the proposed method compared with the standard eigen-features. The classification accuracy is improved by 1.6-4.5% using a supervised classifier.
Location: TE 12 New Biology Building
Literature cited 1: Antonarakis, A.S., Richards, K.S., Brasington, J., 2008.Object-based land cover classification using airborne lidar. Remote Sens. Environ.112 (6), 2988-2998. Axelsson, P., 1999. Processing of laser scanner data-algorithms and applications.ISPRS J., Photogrammetry Remote Sens.54 (2-3), 138-147.
Literature cited 2: Bork, E.W., and Su. J.G., 2007. Integrating LIDAR data and multispectral imagery for enhanced classification of rangeland vegetation: a meta analysis. Remote Sens. Environ.111 (1), 11-24. Brodu, N., Lague, D., 2012.3D terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: applications in geomorphology. ISPRS J. Photogrammetry Remote Sens.68, 121-134.

ID: 59555
Title: A dual quaternion-based, closed-form pairwise registration algorithm for point clouds.
Author: Yongbo Wang, Yunjia Wang, Kan Wu, Huachao Yang, Hua Zhang
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 94. 63-69 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: LiDAR, Registration, Similarity transformation, Rigid transformation, Dual quaternions, Geodetic datum.
Abstract: The representation of similarity transformation in three-dimensional (3D) space, especially of orientation, is crucial issue in navigation, geodesy, Photogrammetry, robot arm manipulation, etc. Considering the large amount of computer resources required by iterative algorithms designed for spatial similarity transformation, the high dependence on initial values of unknown parameters, and the instability of solving transformation parameters for large-angle registration, a closed-form solution for pair wise light detection and ranging (LiDAR) point cloud registration is proposed. In this solution, dual-number quaternions are used to represent the 3D rotation. The relationship between the rotation matrix-based representations is described first. Considering that the same features from two neighboring stations coincide after pairwise registration, a dual quaternion-based error norm, which is associated with the sum of the position errors, is constructed. Based on theory of least squares and by extreme value analysis of the error norm, detailed derivations of the model and the formulas are obtained. Once the similarities between the same features from the two neighboring LiDAR stations are constructed, the rotation matrix, the scale parameter, and the translation vector are simultaneously derived. Two experiments are conducted to verify the feasibility and effectiveness of the proposed algorithm. The proposed algorithm has the advantages of simplicity and ease of implementation, making it better than the traditional methods that use matrices to describe spatial rotation. Moreover, it solves the transformation parameters without the initial estimates of unknown parameters, making it better than iterative algorithms. Most importantly, in contrast to unit quaternion-based algorithms, the proposed algorithm solves seven unknown parameters simultaneously. Therefore, it effectively avoids the accumulation of introduced error in calculation and the negative impact from the inappropriate choice of initial values.
Location: TE 12 New Biology Building
Literature cited 1: Arun, K.S., Huang, T.S., Blostein, S.D., 1987. Least-squares fitting of two 3-D point sets. IEEE Trans.Pattern Anal. Mach. Intell.9 (5), 698-700. Besl, P.J., McKay, N.D., 1992. A method for registration of 3-D shapes. IEEE Trans. Pattern Anal.Mach.Intell.14 (2), 239-256.
Literature cited 2: Chasles, M., 1830. Note sur les proprietes g?n?rales du syst?me de deux corps semblables entreux et places d ' une maniere quelconque dans I ' espace; et sur le deplacement fini ou infiniment petit d ' un corps solide libre.Bull.sci.Math.Astron.Phys.Chim.14 (XIV), 321-326. Daniilidis, K.1999.Hand-eye calibration using dual quaternions.Int.J. Robotics Res.18 (3), 286-298.

ID: 59554
Title: Effect of field plot location on estimating tropical forest above-ground biomass in Nepal using airborne laser scanning data.
Author: Parvez Rana, Lauri Korhonen, Basanta Gautam, Timo Tokola.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 94. 55-62 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: ALS, Field data, Prediction, Sampling design, Tropical forest, Sparse Bayesian method.
Abstract: The prediction of tropical forest attributes using airborne laser scanning (ALS) is becoming attractive as an alternative to traditional field measurements. Area-based ALS inventories require a set of representative field plots from the study area, which may be difficult to obtain in tropical forests with limited accessibility. This study investigates the effect of sample-plot selection in Nepal, based on two accessibility factors: distance to road and degree of slope. The sparse Bayesian method was employed in the model to estimate above-ground biomass (AGB) with an independent validation dataset for model validation. Study findings showed that the sample plot distance and slope had a considerable effect on the accuracy of the AGB estimation, because the forest structure varied according to the level accessibility. Thus, the field sample plots that are used in model construction should cover the full range of sample plot distances and slopes occurring within the area.
Location: TE 12 New Biology Building
Literature cited 1: CAPS, 2008.Churia Area Programme Strategy. Ministry of Forests and Soil Conservation, Government of Nepal. In Collaboration with CARE-Nepal, IUCN-Nepal and WWF-Nepal. http://www.carenepal.org/publication/-CHULI%20program%20strategy.pdf (accessed 02.07.12 ) Dalponte, M., Martinez, C., Rodeghiro, M., Gianelle, D., 2011. The role of ground reference data collection in the prediction of stem volume with ALS data in mountain areas. ISPRS J.Photogramm. Remote Sens.66 (6), 787-797.
Literature cited 2: Drake, J.B., Knox, R.G., Dubayah, R.O., Clark, D.B., Condit, R., Blair, J.B., Hofton, M., 2003. Above-ground biomass estimation in closed canopy Neotropical forests using ALS remote Sensing: factors affecting the generality of relationships. Global Ecol. Biogeogr.12 (2), 147-159. Eid, T., Gobbaken, T., Naesset, E., 2004. Comparing stand inventories based on photo interpretation and laser scanning by mean of cost-plus-loss analysis. Scand. J. Forest Res. 19(6), 512-523.

ID: 59553
Title: Recovery of elevation from estimated gradient fields constrained by digital elevation maps of lower lateral resolution.
Author: Arne Grumpe, Christian Wohler.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 94. 37-54 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: DEM/DTM generation, Surface reconstruction, Image analysis.
Abstract: Depth measurement technique like stereo analysis and laser range scanning often yield a lateral resolution below image resolution. In contrast, shading-based methods estimate the elevation model at image resolution. We present a computationally efficient approach to recover a surface of high vertical and high lateral resolution from a noisy gradient field and independently measured elevation data of lower lateral resolution, relying on a minimization of the mean squared difference between the low-pass component of the surface obtained based on shading information and that of the independently measured elevation data. The presented method is compared to a reference approach that minimizes a weighted sum comprised of the mean squared difference between the low-pass components of the estimated gradient field and the optimized model, respectively. The presented algorithm is applied using lunar orbital image data and stereo elevation data and is evaluated regarding orbital laser altimeter measurements of high vertical accuracy.
Location: TE 12 New Biology Building
Literature cited 1: Agrawal, A., Raskar, R., Chellapa, R., 2006. What is the range of surface reconstructions from gradient field? In: Leonardis, A., Bischof, H., Pinz, A., (Eds), Computer Vision - ECCV 2006 of Lecture Notes in Computer Science, Vol.3951. Springer, Berlin, Heidelberg, pp.578-591. Fassold, H., Danzl. R., Schindler, K., Bischof, H., 2004. Reconstruction of archeological finds using shape from shading. In: 9th Computer Vision Winter Workshop (CVWW), February, Piran, pp.21-30.
Literature cited 2: Frankot, R.T., Chellappa., R., 1988. A method for enforcing integrability in shape from shading algorithms. IEEE Trans. Patt.Anal.Mach.Intell.10 (4), 439-451. Frankot., R.T., Chellappa, R., 1990. Estimation of surface topography from SAR imagery using shape from shading techniques. Artif. Intell.43 (2), 271-310.

ID: 59552
Title: Column-generation kernel nonlocal joint collaborative representation for hyper spectral image classification.
Author: Jiayi Li, Hongyan Zhang, Liangpei Zhang
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 94. 25-36 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Kernel method, Hyper spectral image classification, Joint collaboration model, Column generation.
Abstract: We propose a kernel nonlocal joint collaborative representation classification method based on column generation for hyper spectral imagery. The proposed approach first maps the original spectral space to a higher implicit kernel space by directly taking the similarity measures between spectral pixels as a feature, and then utilizes a nonlocal joint collaborative regression model for kernel signal reconstruction and the subsequent pixel classification. We also develop two kinds of specific radial basis function kernels for measuring the similarities. The experimental results indicate that the proposed algorithms obtain a competitive performance and outperform other state-of -the-art regression-based classifiers and the classical support vector machines classifier.
Location: TE 12 New Biology Building
Literature cited 1: Bi. J., Zhang. T., Bennet, K.P., 2004. Column-generation boosting methods for mixture of kernels. In: Proc. 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 22-25 August, Seatle, W.A., pp.521-526. Boser, B.E., Guyon, I.M., Vapnik., V.N., 1992. A training algorithm for optimal margin classifiers. In: Proc.5th Annual Workshop on Computational Learning Theory, 27-29 July, Pittsburgh, PA, pp.144-152.
Literature cited 2: Chen, Y., Nasrabadi, N.M., Tran, T.D., 2011. Hyperspectral image classification using dictionary-based sparse representation. IEEE Trans. Geosci. Remote Sens.49 (10), 3973-3985. Chen, Y., Nasarbadi, N.M., Tran, T.D., 2013. Hyperspectral image classification via kernel sparse representation. IEEE Trans. Geosci. Remote Sens. 51 (1), 217-231.

ID: 59551
Title: A multi-band approach to unsupervised scale parameter selection for multi-scale image segmentation.
Author: Jian Yang, Peijun Li, Yuhong He.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 94. 13-24 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Object based image analysis, Multi-scale segmentation, Appropriate scale parameter selection, Multi-band, Spectral-angle, Segmentation evaluation.
Abstract: Image segmentation is one of key steps in object based image analysis of very high resolution images. Selecting the appropriate scale parameter becomes a particularly important task in image segmentation. In this study, an unsupervised multi-band approach is proposed for scale parameter selection in the multi-scale image segmentation process, which uses spectral angle to measure the spectral homogeneity of segments .With the increasing scale parameter, spectral homogeneity of segments decreases until they match the objects in the real world. The index of spectral homogeneity is thus used to determine multiple appropriate scale parameters. The performance of the proposed method is compared to single-band based method through qualitative visual interpretation and quantitative discrepancy measures. Both methods are applied for segmenting two images: a QuickBird scene of an urban area within Beijing, China and a Worldview-2 scene of a suburban area in Kashiwa, Japan. The proposed multi-band based segmentation scale parameter selection method outperforms the single-band method with the better recognition for diverse land cover objects in different urban landscapes.
Location: TE 12 New Biology Building
Literature cited 1: Akcay, H.G., Aksoy, S., 2008. Automatic detection of geospatial objects using multiple hierarchical segmentations. IEEE Trans. Geosci. Rem. Sens.46 (7), 2097-2111. Ardila., J.P., Bijker., W., Tolpekin, V.A., Stein, A., 2012. Context-sensitive extraction of tree crown objects in urban areas using VHR satellite images. Int. J. Appl. Earth Observ. Geoinform.15, 57-69
Literature cited 2: Benz. U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M., 2004.Multiresolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J., Photogram. Rem. Sens. 58(3-4), 239-258. Blaschke, T., 2010. Object based image analysis for remote sensing. ISPRS J. Photogram.Rem.Sens.65 (1), 2-16.

ID: 59550
Title: A bottom-up approach to segment individual deciduous trees using leaf-off lidar point cloud data.
Author: Xingcheng Lu, Qinghua Guo, Wenkai Li, Jacob Flanagan
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 94. 1-12 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Lidar, Deciduous forest, Tree segmentation, Intensity, 3-D structure, Botom-up
Abstract: Light Detection and Ranging (Lidar) can generate three-dimensional (3D) point cloud which can be used to characterize horizontal and vertical forest structure, so it has become a popular tool for forest research. Recently, various methods based on top-down scheme have been developed to segment individual tree from lidar data. Some of these methods, such as the one developed by Li et al. (2012), can obtain the accuracy up to 90% when applied in coniferous forests. However, the accuracy will decrease when they are applied in deciduous forest because the interlacing tree branches can incease the difficulty to determine the tree top. In order to solve challenges of the tree segmentation in deciduous forests, we develop a new bottom-up method based on the intensity and 3D structure of leaf-off lidar point cloud data in this study. We applied our algorithm to segment trees in a forest at the Shavers Creek Watershed in Pennsylvania. Three indices were used to assess the accuracy of our method: recall, precision and F-score. The results show that the algorithm can detect 84% of the tree (recall), 97% of the segmented trees are correct (precision) and the overall F-score is 90%. The result implies that our method has good potential for segmenting individual trees in deciduous broadleaf forest.
Location: TE 12 New Biology Building
Literature cited 1: Bortolot, Z.J., Wynne, R.H., 2005. Estimating forest biomass using small footprint LiDar data: an individual tree-based approach that incorporates training data. ISPRS J.Photogramm.Remote Sens.59 (6), 342-360. Brandtberg, T., Warner, T.A.,Landenberger, R.E., McGraw, J.B.,2003. Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America. Remote Sens.Environ.85 (3), 290-303.
Literature cited 2: Chen, Q., Baldocchi, D., Gong, P., Kelly, M., 2006. Isolating individual trees in savanna woodland using small footprint lidar data. Photogramm.Eng.Remote Sens. 72(8), 923-932. Coren, F., Sterzai, P., 2006. Radiometric correction in laser scanning .Int. J. Remote Sens 27 (15), 3097-3104.

ID: 59549
Title: Results of the ISPRS benchmark on urban object detection and 3D building reconstruction.
Author: Franz Rottensteiner, Gunho Sohn, Markus Gerke, Jan Dirk Wegner, Uwe Breitkopf, Jaewook Jung.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 93. 256-271 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Automatic object extraction, 3D building reconstruction, Aerial imagery, Laser scanning, Evaluation, Benchmarking test.
Abstract: For more than two decades, many efforts have been made to develop methods for extracting urban objects from data acquired by airborne sensors. In order to make the results of such algorithms more comparable, benchmarking data sets are paramount importance. Such a data set, consisting of airborne image and landscanner data, has been made available to the scientific community by ISPRS WGIII/4. Researchers were encouraged to submit their results of urban object detection and 3D building reconstruction, which were evaluated based on reference data. This paper presents the outcomes of the evaluation for building detection, tree detection, and 3D building reconstruction. The results achieved by different methods are compared and analysed to identify promising strategies for automatic urban object extraction from current airborne sensor data, but also common problems of state-of the-art methods.
Location: TE 12 New Biology Building
Literature cited 1: Awrengjeb, M., Zhang, C., Fraser, C.S., 2012. Building detection in complex scenes through effective separation of buildings from trees. Photogrammetric Engineering & Remote Sensing 78 (7), 729-745 Bulatov, D., Haufel. G., Meidow, J., Pohl, M., Solbrig, P., Wernerus, P., 2014.Context-based automatic reconstruction and texturing of 3D urban terrain for quick response tasks. ISPRS Journal of Photogrammetry and Remote Sensing 93, 157-170
Literature cited 2: Champion, N., Rottensteiner, F., Matikainen, L., Liang. J., Hyyppa. J., Oslen, B., 2009. A test of automatic building change detection approaches. International Archives of Photogrammetry, Remote Sensing and Spatial Information Systems 38 (part3-W4), 145-150. Cramer, M., 2010. The DGPF test on digital aerial camera evaluation-overview and test design. Photogrammetrie-Fernerkundung-Geoinformation 2, 73-82.

ID: 59548
Title: Detection, segmentation, and classification of 3D urban objects using mathematical morphology and supervised learning.
Author: Andres Serna, Beatriz Marcotegui
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 93. 243-255 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: 3D urban analysis, Laser scanning, Detection, segmentation, Classification, Mathematical morphology, Support vector machine (SVM)
Abstract: We propose an automatic and robust approach to detect, segment and classify urban objects from 3D point clouds. Processing is carried out using elevation images and the result is reprojected onto the 3D point cloud. First, the ground is segmented and objects are detected as discontinuities on the ground. Then, connected objects are segmented using a watershed approach. Finally, objects are classified using SVM with geometrical and contextual features. Our methodology is evaluated on databases from Ohio (USA) and Paris (France). In the former, our method detects 98% of the objects, 78% of them are correctly segmented and 82% of the well-segmented objects are correctly classified. In the latter, our method leads to an improvement of about 15% on the classification step with respect to previous works. Quantitative results prove that our method not only provides a good performance but is also faster than other works reported in the literature.
Location: TE 12 New Biology Building
Literature cited 1: Alexander, C., Tansey, K., Kaduk, J., Holland, D., Tate, N.J., 2010. Backscatter coefficient as attribute for the classification of full-waveform airborne laser scanning data in urban areas. ISPRS J.Photogramm.Rem.Sens.65 (5), 423-432. Anguelov, D., Taskarf, B., chatalbashev, V., Koller, D., Gupta, D., Heitz, G., Ng, A., 2005. Discriminative learning of Markov random fields for segmentation of 3D scan data. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, Vol.2, pp.169-176.
Literature cited 2: Avci, M., Akyurek, Z., 2004. A hierarchical classification of landsat TM imagery for landcover mapping. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.XXXV-B4, pp.511-516. Boulaassal, H., Grussenmeyer, P., Tarsha-Kurdi, F., 2007.Automatic segmentation of building facades using terrestrial laser data. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.xxxvI-3/W52, PP65-70

ID: 59547
Title: A graph edit dictionary for correcting errors in roof topology graphs reconstructed from point clouds.
Author: B. Xiong, S. Oude Elberink, G. Vosselman
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 93. 227-242 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: 3D building reconstruction, Airborne laser scanning, Roof topology graph, Graph edit dictionary, Building primitive library, LOD2.
Abstract: In the task of 3D building model reconstruction from point clouds we face the problem of recovering a roof topology graph in the presence of noise, small roof faces and low point densities. Errors in roof topology graphs will seriously affect the final modeling results. The aim of this research is to automatically correct these errors. We define the graph correction as a graph-to-graph problem, similar to the spelling correction problem (also called the string -to-string problem). The graph correction is more complex than string correction, as the graphs are 2D while strings are only 1D. We design a strategy based on a dictionary of graph edit operations to automatically identify and correct the errors, in the input graph. For each type of error the graph edit dictionary stores a representative erroneous subgraph as well as the corrected version. As an erroneous roof topology graph may contain several errors, a heuristic search is applied to find the optimum sequence of graph edits to correct the errors one by one. The graph edit dictionary can be expanded to include entries needed to cope with errors that were previously not encountered. Experiments show that the dictionary with only fifteen entries already properly corrects one quarter of erroneous graphs in about 4500 buildings, and even half of the erroneous graphs in one test area, achieving as high as a 95% acceptance rate of the reconstructed models.
Location: TE 12 New Biology Building
Literature cited 1: Aho, A.V., Corasick, M.J., 1975. Efficient string matching: an aid to bibliographic search.Commun.ACM 18 (6), 333-340 Barrow, H.G., Popplestone, R., 1971. Relational descriptions in picture processing. Mach.Intell. 6, 377-396.
Literature cited 2: Berger, F., Gritzmann, P., Vries, S., 2004. Minimum cycle bases for network graphs.Algorithmica 40 (1), 51-62.http://dx.doi.org/10.1007/s00453-004-1098-x. Boyer, K.L., KaK, A.C., 1988. Structural stereopsis for 3-D vision.IEEE Trans Pattern Anal.Mach. Intell10 (2), 144-166.

ID: 59546
Title: Cycle graph analysis for 3D roof structure modelling: Concepts and performance
Author: Gamage Sanka Nirodha Perera, Hans-Gerd Maas.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 93. 213-226 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: 3D Roof reconstruction, Airborne laser scanning, Cycle graph, Graph analysis, Regularization, Symmetries
Abstract: The paper presents a cycle graph analysis approach to the automatic reconstruction of 3D roof models from airborne laser scanner data. The nature of convergences of topological relations of plane adjacencies, allowing for the reconstruction of roof corner geometries with preserved topology, can be derived from cycles in roof topology graphs. The topology between roof adjacencies is defined in terms of ridge-lines and step-edges. In the proposed method, the input point cloud is first segmented and roof topology is derived while extracting roof planes from identified non-terrain segments. Orientation and placement regularities are applied on weakly defined edges using a piecewise regularization approach prior to the construction, which assists in preserving symmetries in building geometry. Roof corners are geometrically modelled using the shortest closed cycles and the outermost cycle derived from roof topology graph in which external target graphs are no longer required. Based on test results, we show that the proposed approach can handle complexities with nearly 90% of detected roof faces reconstructed correctly. The approach allows complex height jumps and various types of building roofs to be firmly reconstructed without prior knowledge of primitive building types
Location: TE 12 New Biology Building
Literature cited 1: Brenner, C., 2000. Towards fully automatic generation of city modes.Int.Arch.Photogramm. Remote Sens. Spatial Inform.Sci.33 (Part 3B), 85-92. Brenner, C., 2005. Building reconstruction from images and laser scanning. Int.J. Appl.Earth Observ. Geoinform. 6 (3-4), 187-198.
Literature cited 2: Diestal, R., 2010.Graph Theory, Fourth ed.springer-Verlag, Heidelberg. Dorninger, P., Pfeifer, N., 2008. A comprehensive automated 3d approach for building extraction, reconstruction, and regularization from airborne laser scanning point clouds. Sensors 8 (11), 7323-7343.

ID: 59545
Title: An adaptive approach for the segmentation and extraction of planar and linear/cylindrical features from laser scanning data.
Author: Zahra Lari, Ayman Habib.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 93. 192-212 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Segmentation, Feature extraction, Principal Component Analysis, Spatial domain, Parameter domain, Clustering
Abstract: Laser scanning systems have been established as leading tools for the collection of high density three dimensional data over physical surfaces. The collected point cloud does not provide semantic information about the characteristics of the scanned surfaces. Therefore, different processing techniques have been developed for the extraction of useful information from this data which could be applied for diverse civil, industrial, and military applications. Planar and linear/cylindrical features are among the most important primitive information to be extracted from laser scanning data, especially those collected in urban areas. This paper introduces a new approach for the identification, parametrization, and segmentation of these features from laser scanning data while considering the internal characteristics of the utilized point cloud - i.e., local point density variation and noise level in the dataset. In the first step of this approach, a Principal Component Analysis of the local neighborhood of individual points is implemented to identify the points that belong to planar and linear/cylindrical features and select their appropriate representation model. For the detected planar features, the segmentation attributes are then computed through an adaptive cylinder neighborhood definition. Two clustering approaches are then introduced to segment and extract individual planar features in the reconstructed parameter domain. For the linear/cylindrical features, their directional and positional parameters are utilized as the segmentation attributes. A sequential clustering technique is proposed to isolate the points which belong to individual linear/cylindrical features through directional and positional attribute subspaces. Experimental results from simulated and real datasets demonstrate the feasibility of the proposed approach for the extraction of planar and linear/cylindrical features from laser scanning data.
Location: TE 12 New Biology Building
Literature cited 1: Al-Durgham, M., Habib, A., 2013. A framework for the registration and segmentation of heterogeneous LiDAR data. Photogramm. Eng.Remote Sens.79 (2). 135-145. Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., Wu, A., 1994.An optimal algorithm for approximate nearest neighbor searching. In: proceedings of the 5th Annual ACM-SIAM Symposium on Discrete Algorithms, Philadelphia,PA, USA,pp.573-582.
Literature cited 2: Axelsson, P., 1999.Processing of laser scanner data-algorithms and applications.ISPRS J.Photogramm. Remote Sens.54 (2-3), 138-147. Belton., D., Lichti., D.D.,2006. Classification and segmentation of terrestrial laser scanner point clouds using local variance information. Int.Arch.Photogramm., Remote Sens. Spatial Inf.Sci.36 (part 5), 44-49.

ID: 59544
Title: Automatic representation and reconstruction of DBM from LiDAR data using Recursive Minimum Bounding Rectangle
Author: Eunju Kwak, Ayman Habib.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 93. 171-191 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: LIDAR, Building, Modeling, Reconstruction, Automation, Generalization.
Abstract: Three -dimensional building models are important for various applications, such as disaster management and urban planning. The development of laser scanning sensor technologies has resulted in many different approaches for efficient building model generation using LiDAR data. Despite this effort, generation of these models lacks economical and reliable techniques that fully exploit the advantage of LiDAR data. Therefore, this research aims to develop a framework for fully-automated building model generation by integrating data-driven and model methods using LiDAR datasets. The building model generation starts by employing LiDAR data for building detection and approximate boundary determination. The generated building boundaries are then integrated into a model -based processing strategy because LiDAR derived planes show irregular boundaries due to the nature of LiDAR point acquisition. The focus of the research is generating models for the buildings with right-angled -corners, which can be described with a collection of rectangles under the assumption that the majority of the buildings in urban areas belong to this category. Therefore, by applying the Minimum Bounding Rectangle (MBR) algorithm recursively, the LiDAR boundaries are decomposed into sets of rectangles for further processing. At the same time, the quality of the MBRS is examined to verify that the buildings, from which the boundaries are generated, are buildings with right-angled-corners. The parameters that define the model primitives are adjusted through a model-based boundary fitting procedure using LiDAR boundaries. The level of details in the final Digital Building Model is based on the number of recursions during the MBR processing, which in turn are determined by the LiDAR point density. The model-based boundary fitting improves the quality of generated boundaries and as seen in experimental results, the quality depends on the average LiDAR point spacing. This research thus develops an approach which not only automates the building model generation, but also achieves the best accuracy of the model while utilizing only LiDAR data.
Location: TE 12 New Biology Building
Literature cited 1: Arefi, H., Engels J., Hahn, M., 2008. Levels of detail in 3D building reconstruction from LiDAR data. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Siences.Beijjing, pp 485-490 Awrangjeb, M., Ravanbakshsh, M., Fraser, C.S., 2010.Automatic detection of residential buildings using LiDAR data and multispectral imagery. ISPRS J. Photogramm. Remote Sens, 65,457-467.
Literature cited 2: Besi, P.J., Jain., R.C., 1988. Segmentation through variable -order surface fitting. IEEE Trans. Pattern Anal. Mach. Intell.10, 167-192. Brenner, C., 2005. Building reconstruction from images and laser scanning. Int.J. Appl.Earth Obs. Geoinform.6, 187-198.

ID: 59543
Title: Context-based automatic reconstruction and texturing of 3D urban terrain for quick-response tasks.
Author: Dimitri Bulatov, Gisela Haufel, Jochen Meidow, Melanie Pohl, Peter Solbrig, Peter Wernerus
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B.V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing.Vol. 93. 157-170 (2014)
Subject: Photogrammetry and Remote Sensing
Keywords: Building detection, Building reconstruction, Pose estimation, Simulation, Texturing, Urban terrain.
Abstract: Highly detailed 3D urban terrain models are the base for quick response tasks with indispensable human participation, e.g. disaster management. Thus, it is important to automate and accelerate the process of urban terrain modeling from sensor data such that the resulting 3D model is semantic, compact, recognizable, and easily usable for training and simulation purposes. To provide essential geometric attributes, buildings and trees must be identified among elevated objects in digital surface models. After building ground-plan estimation and roof details analysis, images from oblique airborne imagery are used to cover buildings faces with up to date texture thus achieving a better recognizably of the model. The three steps of the texturing procedure are sensor pose estimation, assessment of polygons projected into the images, and texture synthesis. Free geographic data, providing additional information about streets, forest areas, and other topographic object types, suppress false alarms and enrich the reconstruction results.
Location: TE 12 New Biology Building
Literature cited 1: Baillard, C., Zisserman, A., 2000. A plane-sweep strategy for the 3D reconstruction of buildings from multiple images. Int. Arch. Photogramm. Rem. Sensing Spatial Inf. Sci 33 (part B2), 56-62. Beder, C., Steffen, R., 2008. Incremental estimation without specifying a-priori covariance matrices for the novel parameters. In: Proc. Of VLMP Workshop on IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, pp.1-6
Literature cited 2: Bodensteiner, C., Arens, M., 2012. Real-time 2D video/3D LiDAR registration. In: IEEE 21 st International conference on Pattern Recognition, Tsukuba (Japan), pp 2206-2209 Bohm,J.,2004.Multi-imagefusionforocclusion-freefa?adetexturing.Int.Arch. Photogramm.Rem.Sensing Spatial Inf.Sci. 35(5), 867-872.