ID: 58101
Title: Diverse responses of vegetation production to interannual summer drought in North America
Author: Chaoyang Wu, Jing M Chen
Editor: F van der Meer
Year: 2013
Publisher: Elsevier, Vol 21, April 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Summer drought, Soil moisture, forests, vegetation production, climate change
Abstract: Droughts are projected to occur more frequently with future climate change of rising temperature and low precipitation. However, its impact on regional and global vegetation production is not well understood, which in turn contributes to uncertainties to model carbon sequestration under drought scenarios. Using long-term continuous eddy covariance measurements (168 site-year). we present an analysis of the influences of interannual summer drought on vegetation production across 29 sites representing diverse ecoregions and plant functional types in NOrth America. Results showed that interannual summer drought, which was evaluated by the increase in summer temperature or decrease in soil moisture, would cause reductions of both summer gross primary production (GPP) and net ecosytem production (NEP) in non-forest sites (e.g grasslands and crops). On the contrary, forest ecosystems presented a very different pattern. For evergreen forests, lower summer soil moisture decreased both GPP and NEP; however, higher summer temperature only reduced NEP with no apparent impacts on GPP. Furthermore, summer drought did not show evident impacts on either summer GPP or NEP in deciduous forest, suggesting a better potential of deciduous forests in resisting summer drought and accumulating carbon form atmosphere. These observations imply diverse responses of vegetation production to interannual summer drought and accumulating carbon from atmosphere. These observations imply diverse responses of vegetation production to interannual summer drougt and such features would be useful to improve the strengths and weaknesses of ecosystem models to better comprehend the impacts of summer drought with future climate change.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58100
Title: Development of a coordinate transformation method for direct georeferencing in map projection frames
Author: Haitao Zhao, Bing Zhang, Changshan Wu, Zhengli Zuo, Zhengchao Chen
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier, Vol 77, March 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of photogrammetry and Remote Sensing
Keywords: Coordinate transformation, GPS/INS, local tangent frame, Map projection frame, orientation angles, direct georeferencing
Abstract: This paper develops a novel Coordinate Transformation method (CT-method), with which the orientation angles (roll, pitch, heading) of the local tangent frame of the GPS/INS system are transformed into those (omega, phi, Kappa) of the map projection frame for direct georeferencing (DG). Especially, the orientation angles in the map projection frame were derived from a sequence of coordinate transformations. The effectiveness of orientation angles transformation was verified through comparing with DG results obtained from conventional methods (Legat method1 and POSPac method2) using empirical data. Moreover, the CT-method was also validated with simulated data. One advantage of the proposed method is that the orientation angles can be acquired simultaneously while calculating position elements of exterior orientation (EO) prameters and auxillary points coordinates by coordinate transformation.
These three methods were demonstrated and compared using empirical data. Empirical results show that the CT-method is both as sound and effective as Legat method. Compared with POSPac method, the CT-method is more suitable for calculating EO parameters for DG in map projection frames. DG accuracy of the CT-method and Legat method are at the same level. DG results of all these three methods have systematic errors in height due to inconsistent length projection distortion in the vertical and horizonal components, and these errors can be significantly reduced using the EO height correction technique in Legat ' s approach. Similar to the results obtained with empirical data, teh effectivenss of the CT-method was also proved with simulated data.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58099
Title: Remote sensing of seasonal variability of fractional vegetation cover and its object-based spatial pattern analysis over mountain areas0
Author: Guijun Yang, Ruiliang Pu, Jixian Zhang, Chunjiang Zhao, Haikuan Feng, Jihua Wang
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier, Vol 77, March 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of photogrammetry and Remote Sensing
Keywords: FVC, topographic and atmospheric effect, segmentation, landscape analysis, Landsat TM image, patch analysis
Abstract: Fractional vegetation cover (FVC) is an important indicator of mountain ecosystem status. A study on the seasonal changes of FVC can be benficial for regional eco-environmental security, which contributes to the assessment of mountain ecosystem recovery and supports mountain forest planning and landscape reconstruction around megacities, for example, Beijing, China. Remote sensign has been demonstrated to be one of the most powerful and feasible tools for the investigation of mountain vegetation. However, topographic and atmospheric effects can produce enormous errors in the quantitative retrieval of FVC data from satellite images of mountainous areas. Moreover, the most commonly used analysis approach for assessing FVC seasonal fluctuations is based on per-pixel analysis regardless of the spatial context, which results in pixel-based FVC values that are feasible for landscape and ecosystem applications. To solve these problems, we proposed a new method that incorporates theuse of a revised physically based (RPB) model to correct both atmopsheric and terrain-caused illumination effects on Landsat images, an improved vegetation Index (VI)-based technique for estimating the FVC and an adaptive mean shift approach for object-based FVC segmentation. An array of metrics for segmented FVC analyses, including a variety of area metrics, patch metrics, shpae metrics and diversity metrics, was generated. On the basis of the individual segmented FVC values and landscape metrics from multiple images of different dates, remote sensing of the seasonal variabilityof FVC was conducted over the mountainous area of Beijing, China. The experimental results indicate that (a) the mean value of the RPB-NDVI in all seasons was increased by approximately 10% compared with that of the atmospheric correction-NDVI; (b) a strong consistency was demonstrated between ground - based FVC observations and FVC estimated through remote sensing technology (R2 = 0.8527, RMSE = 0.0851); and c) seasonal changes in the landscape characteristics existed, and the landscape diversity reached its in May and June in the study area.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58098
Title: Learning with transductive SVM for semisupervised pixel classification of remote sensing imagery
Author: Ujjwal Maulik, Debasis Chakraborty
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier, Vol 77, March 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of photogrammetry and Remote Sensing
Keywords: Pixel classification, support vector machines, Remote snsing satellite images, Quadratic programming, semisupervised classification, Transductive learning
Abstract: Land cover classification using remotely sensed data requries robust classification methods for the accurate mapping of complex land cover area of different categories. In this regard, support vector machines (SVMs) have recently received increasing attention. However, small number of training samples remains a bottleneck to design suitable supervised classifiers. On the other hand, adequate number of unlabeled data is available in remote sensing images which can be employed as additional source of information about margins. To fully leverage all of the precious unlabeled data, integration of filtering in a transductive SVM is proposed.
Using two labeled image datasets of small size and two large unlabeled image datasets, the effectiveness of the proposed emthod is explored. Experimental results show that the proposed technique achieves average overall accuracies of around 4.5-7.8%, 0.8-2.6% and 0.9-2.25 more than the standard inductive SVM (ISVM), progressive transductive SVM (PTSVM) and low density separation (LDS) classifiers, respectively on larger domains in case of labeled datasets. Using image datasets, visual interpretation from the classified images as well as the segmentation quality reveal that the proposed method can efficiently filter informative data from the unlabelled samples.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58097
Title: Extracting polygonal building footprints from digital surface models: A fully-automatic global optimization framework
Author: Mathieu Bredif, Olivier Tournaire, Bruno Vallet, Nicoles Champion
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier, Vol 77, March 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of photogrammetry and Remote Sensing
Keywords: Urban Building Modeling, Geometry Analysis, DEM/DTM
Abstract: This paper presents a fully automatic framework to extract building footprints from a Digital surface Model (DSM).The proposed approach may be decomposed in two steps, each of them relying on a global optimization solver. The first step aims to extract rectangular building footprints directly from the DSM using a Marked Point Process (MPP) of rectangles. We introduce an energy that prevents overlapping rectangles adn aligns rectangle edges with DSM discontinuities. This energy is then embedded in a RJMCMC sampler coupled with a simulated annealing to find its global optimum. Then, the second step of our framework refines these extracted rectangles into polygonal building footprints. We first create an arrangement of line segments supporting the rectangle edges. The dual graph of this arrangements is then considered in a maximum flow optimization scheme to remove edges in the arrangement which do not correspond to building edges in the DSM. Finally, 3D results illustrate a fully automatic process to build a 3D city model from a DSM only.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58096
Title: The influence of scan mode and circle fitting on tree stem detection, stem diameter and volume extraction from terrestrail laser scans
Author: Pyare Pueschel, Glenn Newnham, Gilles Rock, Thomas Udelhoven, Willy Werner, Joachim Hill
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier, Vol 77, March 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of photogrammetry and Remote Sensing
Keywords: Terrestrial laser scanning (TLS), forest inventory, stem detection, stem diameter, stem volume
Abstract: Terrestrial laser scanning (TLS) has been used to estimate a number of biophysical and structural vegetation parameters. Of these stem diameter is a primary input to traditional forest inventory. While many experimental studies have confirmed the potential for TLS to sucessully extract stem diameter, the estimation accuracies differ strongly for these studies- due to differences in experimental design, data processing and test plot characteristics. In order to provide consistency and maximize estimation accuracy, a systematic study into the impact of these variables is required. To contribute to such an approach, 12 scans were acquired with a FARO photon 120 at two test plots (Beech, Douglas fir) to assess the effects of scan mode and circle fitting on the extraction of stem diameter and volume. An automated tree stem detection algorithm based on the range images of single scans was developed and applied to the data. Extraction of stem diameter was achieved by slicing the point cloud and fitting circles to the slices using three different algorithms (Lemen,Pratt and Taubin), resulting in diameter profiles for each detected tree. Diameter at breast height (DBH) was determined using both the single value for the diameter fitted at the nomial breast height and by a linear fit of the stem diameter vertical profile. The latter is intended to reduce the influence of outliers and errors in the ground level determination. TLS-extracted DBH was compared to tape-measured DBH. Results show that tree stems with an unobstructed view to the scanner can be successfully extracted automatically from range images of the TLS data with detection rates of 94% for Beech and 96% for Douglas fir. If occlusion of trees is accounted for stem detection rates decrease to 85% (Beech) and 84% (Douglas fir). As far as the DBH estimation is concerned, both DBH extraction methods yield estimates which agree with reference measurements, however, the linear fit based approach proved to be more robust for the single scane DBH extraction (RMSE range 1.39 -1.74 cm compared to 1.47-2.43 cm). With regard to the different circle fit algorithms applied, the algorithm by Lemen showed the best overall performance (RMSE range 1.39-1.65 cm compared to 1.49 - 2.43 cm). The Lemen algorithm was also found to be more robust in case of noisy data. Compared to the single scans, the DBH extraction from the merged scan data proved to be superior with significant lower RMSE ' s (0.66-1.21 cm). The influence of scan mode and circle fitting is reflected in the stem volume estimates, too. Stem volumes extracted from the single scans exhibit a large variability with deviations from the reference volumes ranging from -34% to 44%. By contrast volumes extracted from the merged scans only vary weakly (-2% to 6%) and show a marginal influence of circel fitting.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58095
Title: Improving Cartosat-1 DEM accuracy using synthetic stereo pair and triplet
Author: D Giribabu, S Srinivasa Rao, Y V N Krishna Murthy
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier, Vol 77, March 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of photogrammetry and Remote Sensing
Keywords: Cartosat-1, Forward/reverse stereo, Synthetic stereo pairs, Triplet/tri-stereoscopy, Image matching, occlusions, Himalayan mountains, DEM
Abstract: Cartosat-1 is the first Indian Remote Sensing Satellite capable of providing along-track stereo images. Cartosat-1 provides forward stereo images with look angles +260 and -50 with respect to nadir for generating Digital Elevation Models (DEMs) , Orthoimages and value added products for various applications. A pitch bias of -210 to the satellite resulted in giving reverse tilt mode stereo pair with lood angles of +50 and -260 with respect to nadir. This paper compares DEMs generated using forward, reverse and other possible synthetic stereo paris for two different types of topographies. Stereo triplet was used to generate DEM for Himalayan mountain topography to overcome the problem of occlusions.
For flat to undulating topography it was shown that using Cartosat-1 synthetic stereo pair with look angles of -260 and +260 will produce improved version of DEM. Planimetric and height accuracy (Root Mean Square Error (RMSE) of less than 2.5 m and 2.95m respectively were obtained and qualitative analysis shows finer details in comparison with other DEMs. For rugged terrain and steep slopes of Himalayan mountain topography simple stereo pairs may not provide reliable accuracies in DEMs due to occlusions and shadows. Stereo triplet from Cartosat-1 was used to generate DEM for moutainous topography. This DEM Shows better reconstruction of elevation model even at occluded region when compared with simple stereo pair based DEM. Planimetric and height accuracy (RMSE) of nearly 3 m were obtained and qualitative analysis shows reduction of outliers at occluded region.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58094
Title: An improved simple morphological filter for the terrain classification of airborne LIDAR data
Author: Thomas J Pingel, Keith C Clarke, William A McBride
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier, Vol 77, March 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of photogrammetry and Remote Sensing
Keywords: LIDAR, classification, algorithms, DEM/DTM, virtual reality
Abstract: Terrain classification of LIDAR point clouds is a fundamental problem in the production of Digital Elevation Models (DEMs). The Simple Morphological Filter (SMRF) addresses this problem by applying image processing techniques to the data. This implementation uses a linearly increasing window and simple slope thresholding, along with a novel application of image inpainting techniques. When tested against the ISPRS LIDAR refernce dataset, SMRF achieved a mean 85.4% Kappa score when using a single parameter set and 90.02% when optimized. SMRF is intended to serve as a stable base from which more advanced progressive filters can be designed. This approach is particularly effective at minimizing Type I error rates, while maintaining acceptable Type II error rates. As a result, the final surface rpeserves subtle surface variatio in the form of tracks and trails that mke this approach ideally suited for the production of DEMs used as ground surfaces in immersive virtual environments.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58093
Title: Detection and 3D reconstruction of traffic signs from multiple view color images
Author: Bahman Soheilian, Nicolas Paparoditis, Bruno Vallet
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier, Vol 77, March 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of photogrammetry and Remote Sensing
Keywords: Traffic sign, Color segmentation, Geometric shape estimation, Template matching, Constrained multi-view reconstruction
Abstract: 3D reconstruction of traffic signs is of great interest in many applications such as image-based localization and navigation. In order to reflect the reality, the reconstruction process should meet both accuracy and precision. In order to reach such a valid reconstruction from calibration multi-view images, accurate and precise extraction of signs in every individual view is a must. This paper presents first an automatic pipeline for identiying and extracting the silhouette of signs in every individual image. Then, a multi-view constrained 3D reconstruction algorithm provides an optimum 3D silhouette for the detected signs. The first step called detection, tackles with a color-based segmentation to generate ROIs (Region of Interests) in image. The shape of evey ROI is estimated by fitting an ellipse, a quadrilateral or a traingle to edge points. A ROI is rejected if none of the three shapes can be fitted sufficiently precisely. Thanks to the estimated shape the remained candidates ROIs are rectified to remove the perspective distortion and then matched with a set of reference signs using textual information. Poor matches are rejected and the types of remained ones are identified. The output of the detection algorithm is a set of identified road signs whose silhouette in image plane is represented by a ellipse, a quadrilateral or a triangle. The 3D reconstruction process is based on a hypothesis generation and verification. Hypotheses are generated by a stereo matching approach taking into account epipolar geometry and also the similarity of the categories. The hypotheses that are plausibly correspond to the same 3D road sign are identified and grouped during this process. Finally, all the hypotheses of the same group are merged to generate a unique 3D road sign by a multi-view algorithm integrating a priori knowledge about 3D shape of road signs as constraints. The algorithm is assessed on real and synthetic images and reached and average accuracy of 3.5 cm for position and 4.50 for orientation.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58092
Title: Historic building information modelling - Adding intelligence to laser and image based surveys of European classical architecture
Author: Maurice Murphy, Eugene McGovern, Sara Pavia
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier, VOl 76, February 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of photogrammetry and Remote Sensing
Keywords: CAD, Cultural heritage, Modelling, Architecture, Building, software
Abstract: Historic Building Information Modelling (HBIM) is a novel prototype library of parametric objects, based on historic architectural data and a system of cross platform programmes for mapping parametric objects onto point cloud and image survey data. The HBIM process begins with remote collection of survey data using a terrestrial laser scanner combined with digital photo modelling. The next stage involves the design and construction of a parametric of a parametric library of objects, which are based on the manuscripts ranging from Vitruvius to 18th century architectural pattern books. In building parametric objects, the problem of file format and exchange of data has been overcome within the BIM ArchiCAD software platform by using geometric descriptive language (GDL). The plotting of parametric objects onto the laser scan surveys as building components to create or form the entire building is the final stage in the reverse engineering process. The final HBIM product is the creation of full 3D models including detail behind the object ' s surface concerning its methods of construction and material make-up. The resultant HBIM can automatically create cut sections, details and schedules in addition to the orthographic projections and 3D models (wire frame or textured) for both the analysis and conservation of historic objects, structures and environments.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58091
Title: One billion points in the cloud- an octree for efficient processing of 3D laser scans
Author: Jan Elseberg, Dorit Borrmann, Andreas Nuchter
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier, VOl 76, February 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of photogrammetry and Remote Sensing
Keywords: Octree, Tree data structure, data compression, Frustum culling, Ray casting, RANSAC, Nearest neighbor search
Abstract: Automated 3-dimensional modelling pipelines include 3D scanning, registration, data abstraction, and visualization. All steps in such a pipeline require the processing of a massive amount of 3D data, due to the ability of current 3D scanners to sample environments with a high density. The increasing sampling rates make it easy to acquire Billions of spatial data points. This paper presents algorithms and data structures for handling these data. We propose an efficient octree to store and compress 3D data without loss of precision. We demonstrate its usage for an exchange file format, fast point cloud visualization, sped-up 3D scan matching, and shape detection algorithms. We evaluate our approach using typical terrestrial laser scans.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58090
Title: Automatic orientation and 3D modelling from markerless rock art imagery
Author: Jose L Lerma, Santiago Navarro, Miriam Cabrelles, Ana E Segui, David Hernandez-Lopez
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier, VOl 76, February 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of photogrammetry and Remote Sensing
Keywords: Automation orientation matching, Bundle adjustment, Close range imagery, Terrestrial laser scanning
Abstract: This paper investigates the use of two detectors and descriptors on image pyramids for automatic image orientation and generation of 3D models. The detectors and descriptors replace manual measurements and are used to detect, extract and match features across multiple imagery. The Scale-Invariant Feature Tarnsform (SIFT) and the Speeded Up Robust Features (SURF) will be assessed based on speed, number of features, matched features, and precision in image and object space depending on the adopted hierarchical matching scheme. The influence of applying in addition Area Based Matching (ABM) with normalised cross-correlation (NCC) and least squares matching (LSM) is also investigated. The pipeline makes use of photogrammetric adn computer vision algorithms aiming minimum interaction and maximum accuracy from a calibrated camers. Both the exterior orientation parameters and the 3D coordinates in object space are sequentially estimated combining relative orientation, single space resection and bundle adjustment. The fully automatic image-based pipeline presented herein to automate the image orientation step of a sequence oof terrestrial markerless imagery is compared with manual bundle block adjustment and terrestrial laser scanning (TLS) which serves as ground truth. The benefits of applying ABM after FBM will be assessed both in image and object space for the 3D modelling of a complex rock art shelter.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58089
Title: Ground filtering and vegetation mapping using multi-return terrestrial laser scanning
Author: Francesco Pirotti, Alberto Guarnieri, Antonio Vettore
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier, VOl 76, February 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of photogrammetry and Remote Sensing
Keywords: Terrestrial laser scanning, vegetation mapping, spatial classification, point cloud processing, DEM/DTM
Abstract: Discriminating laser scanner dat points belonging to ground from points above-ground (vegetation or buildings) is a key issue in research. Methods for filtering points into ground and non-ground classes have been widely studied mostly on datasets derived from airborne laser scanners, less so for terrestrial laser scanners. Recent developments in terrestrial laser sensors (longer ranges, faster acquisition and multiple return echoes) has aroused greater interest for surface modelling applications. The downside of TLS is that a typical dataset has high variability in point density, with evident side-effects on processing methods and CPU-time. In this work we use a scan dataset form a sensor which returns multiple target echoes, in this case providing more than 70 million pionts on our stuy site. The area presents low, medium and high vegetation , undergrowth with varying density, as well as bare ground with varying morphology (i.e very steep slopes as well as flat areas). We test an integrated work-flow for defining a terrain and surface model (DTM and DSM) and successively for extracting information on vegetation density and height distribution on such a complex environment. Attention was given to efficiency and speed of processing. The method consists on a first step which subsets the original points to define ground candidates by taking into account the ordinal return number and the amplitude. A custom progressive morphological filter (opening operation) is applied next, on ground candidate points using a multidimensional grid to account for the fallout in point density as a function of distance from scanner. Vegetation density mapping over the area is then estimated using a weighted ratio of point counts in the tri-dimensional space over each cell. The overall result is a pipeline for processing TLS points clouds with minimal user interaction, producing a Digital Terrain Model (DTM), a Digital Surface Model (DSM), a vegetation density map and a derived Canopy Height Model (CHM). These products are of high importance for many applications ranging from forestry to hydrology and geomorphology.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58088
Title: Combining terrestrial stereophotogrammetry, DGPS and GIS-based 3D voxel modellign in the volumetric recording of archaeological features
Author: Hector A Orengo
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier, VOl 76, February 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of photogrammetry and Remote Sensing
Keywords: Terrestrial stereoscopic, GIS, GPS, recording, archaeology, orthoimage, DSM
Abstract: Archaeological recording of structures and excavations in high mountain areas is greatly hindered by the scarce availability of both space, to transport material, and time. The Madriu-Perafita-Claror, InterAmbAr and PCR Mont Lozere high mountain projects have documented humdreds of archaeological structures and carried out many archaeological excavations. These projects required the development of a technique which could record both structures and the process of an archaeological excavation in a fast and reliable manner.
The combinations of DGPS, close-range terrestrial stereophotogrammetry and voxel based GIS modelling offered a perfect solution since it helped in developing a strategy which would obtain all the required data on-site fast and with a high degree of precision. These data are treated off-site to obtain georeferenced orthoimages covering both the structures and the excavation process from which site and excavation plans can be created. The proposed workflow outputs also include digital surface models and volumetric models of the excavated areas from which topography and archaeological profiles were obtained by voxel-based GIS procedures. In this way, all the graphic recording required by standard arechaeological practices was met.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58087
Title: Segmentation of terrestrial laser scanning data using geometry and image information
Author: Shahar Barnea, Sagi Filin
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier, VOl 76, February 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of photogrammetry and Remote Sensing
Keywords: Segmentation, terrestrial laser scanner, point cloud, data fusion
Abstract: Terrestrial lase scanning is becoming a standard technology for 3D modeling of complex scenes. Laser scans contain detailed geometric information, but still require interpretation of the data for making it useable for mapping purposes. A fundamental step in the transformation of the data into objects involves their segmentation into consistent units. Such units should follow some predefined rules, and result in salient regions guided by the desire that the individual segments represent object or object-parts within the scene. Nonetheless, due to the scene complexity and the variety of objects, a segmentation using only a single cue does not suffice. Considering the availability of additional data sources such as color images, more information can be integrated in the data partitioning process and ultimately into the reconstruction scheme. We propose segmentation of terrestrial laser scanning data by the integration of range and color content and by using multiple cues. This concept raises questions regarding their mode of integration, and definition of the expected outcome. We show, that while individual segmentation based on given cues have their own limitations, their integration provide a more coherent partitioning that has better potential for further processing. Experiments show that the proposed segmentation methodology yield physically meaningful segments, which surpass those obtained via segmentation of the individual channels.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None