ID: 58713
Title: Partial iterates for symmertrizing non-parametric color correction.
Author: Bruno Vallet, Laman Lelegard.
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier B. V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 82, pp. 93-101 (2013)
Subject: ISPRS Journal of Photogrammetry & Remote Sensing
Keywords: Mosaic, Color correction. Radiometry, Fusion, Image.
Abstract: Mosaic generation is a central tool in various fields ranging way beyond the scope of photogrammetry and requires the radiometry and color of the images to be corrected. Correction can either be done by a global parametric approach (looking for an optimal gain or gamma for each image of the mosaic), or by iteratively correcting image pairs with a non parametric approach. Such non-parametric approaches allow for much finer correction but are asymmetric, i.e. they require the choice of a source image that will be corrected to match a target image. Thus the result on the whole mosaic will be very dependant on the order in which images are corrected. In this paper, we propose to use partial iterates to symmetrize non-parametric correction in order to solve this problem. Partial iterates formalize what partially applying a bijective function means and we explain how this can be done in both the continuous and discrete domain. This mechanism is applied to a simple non-parametric appraoach (histogram transfer of the luminance) to show its potential.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58712
Title: Evaluating the capabilities of Sentimental-2 for quantitative estimation of biophysical variables in vegetation.
Author: William James Frampton, Jadunandan Dash, Gary Watmough, Edward James Milton.
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier B. V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 82, pp. 83-92 (2013)
Subject: ISPRS Journal of Photogrammetry & Remote Sensing
Keywords: Vegetation, Sentinel-2, Chlorophyll, Red-Edge, LAI
Abstract: The red edge position (REP) in the vegetation spectral reflectance is a surrogate measure of vegetation chloropyll content, and hence can be used to monitor the health function of vegetation. The Multi-Spectral Instrument (MSI) aboard the future ESA Sentinel-2 (S-2) satellite will provide the opportunity for estimation of the REP at much higher spatial resolution (20m) than has been previously possible with spaceborne sensors such as Medium Resolution Imaging Spectometer (MERIS) aboard ENVISAT. This study aims to evaluate the potential of S-2 MSI sensor for estimation of canopy chloropyll content, leaf area index (LAI) and leaf chlorophyll concentration (LCC) using data from multiple field campaigns are results from SEN3Exp in Barrax. Spain composed of 35 elementary sampling units (ESUs) of LCC and LAI which have been assessed for correlation with simulated MSI data using a CASI airborne imaging spectrometer. Analysis also presents results from SicilyS2EVAL, a campaign consisting of 25 ESU ' s in Sicily, Italy supported by simultaneous Spectim Asia-Eagle data acquisition. In addition, these results were compared to outputs from the PROSAIL model for similar values of biophysical variables using these combined datasets through investigating the performance of the relevant Vegetation Indicies (VIs) as well as presenting the novel Inverted Red-Edge Chlorophyll Index (IRECI) and Sentinel-2 Red Edge Position (S2REP), Results indicated significant relationships between both canopy chlorophyll content and LAI for simulated MSI data using IRECI or the Normalised Difference Vegetation Index (NDVI) while S2REP and the MERIS Terrestial Chlorophyll Index (MTCI) were found to have the strongest correlation for retrieval of LCC.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58711
Title: Analysis of full-waveform LiDAR data for classification of an orange orchard scene.
Author: Karolina D Fieber, Ian J Davenport, James M Ferryman, Robert J Gurney, Jeffrey P Walker, Jorg M Hacker.
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier B. V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 82, pp. 63-82 (2013)
Subject: ISPRS Journal of Photogrammetry & Remote Sensing
Keywords: Full-waveform, LiDAR, Backscattering coefficient, Classification, Reflectance, Vegetation.
Abstract: Full-waveform laser scanning data acquired with a Riegl LMS-Q560 instrument were used to classify an orange orchard into orange trees, grass and ground using waveform parameters alone. Gaussian decomposition was performed on this data
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58710
Title: Glacier surface velocity estimation using repeat TerraSAR-X images: Wavelet- vs. correlation-based image matching
Author: Adrain Schubert, Annina Faes, Andreas Kaab, Erich Meier.
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier B. V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 82, pp. 49-62 (2013)
Subject: ISPRS Journal of Photogrammetry & Remote Sensing
Keywords: Image matching, Feature tracking, Glacier surface velocity, Aletsch, TerraSAR-X, Synthetic Aperture Radar, Wavelet decomposition.
Abstract: For the observation and monitoring of glacier surface velocity (GSV), remote sensing is an increasingly suitable tool thanks to the high temporal and spatial resolution of the data. Radar sensors have the specific advantage over optical sensors of being nearly weather and time-independent.
Two image pairs seperated by 11 days, acquired with the high-resolution spotlight (HS) and stripmap (SM) modes of Gernan sensor TerraSAR-X, were used to estimate GSV over Switzerland ' s Aletsch Glacier. The SM mode covers larger ground swaths, making it more suitable for glacier-wide observations, while the HS images cover less area but offer the highest possible spatial resolution, approximately 1?1m on the ground. The images were acquired during the summer to maximise feature visibility by minimal snow cover.
GSV estimation was performed using two methods, the comparison of which was a major goal of this study: traditional cross-correlation optimization and a dense image matching algorithm based on complex wavelet decomposition. Each method was found to have unique advantages and disadvantages, but it was concluded that for GSV monitoring, cross-correlation is probably preferable to the wavelet-based approach. While it generates fewer estimates per unit area, this not necessarily a critical requirement for all glaciological applications, and the method requires less initial "tuning" (calibration) than the wavelet algorithm, making it a slightly better tool in operational contexts. Also, the use of the highest resolution spotlight datasets is recommended over stripmap mode images when large-area coverage is less critical. The comparative lack of visible features at the resolution of the stripmap images made reliable GSV estimation difficult, with the exception of several small areas dominated by large crevasses.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58709
Title: 3-D voxel-based solid modelling of a broad-leaved tree for accurate volume estimation using portable scanning lidar.
Author: Fumiki Hosoi, Yohei Nakai, Kenji Omasa.
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier B. V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 82, pp. 41-48 (2013)
Subject: ISPRS Journal of Photogrammetry & Remote Sensing
Keywords: Portable ground based scanning lidar, Solid model, Voxel, Woody material volume
Abstract: We developed a method to produce a 3-D voxel based model of a tree based on portable scanning lidar data for accurate estimation of the volume of the woody material. First, we obtained lidar measurements with a high laser pulse density from several measurement positions around the target, a Japanese zelkova tree. Next, we converted lidar-derived point-cloud data for the target in voxels. The voxel size was 0.5cm ? 0.5cm ? 0.5cm. Then, we used differences in the spatial distribution of voxels to seperate the stem and the large branches (diameter > 1cm) from small branches (diameter ? 1cm). We classified the voxels into sets corresponding to the stem and to each large branch and then interpolated voxels to fill out their surfaces and their interiors. We then merged the stem and large branches with the small branches. The resultant solid model of the entire tree was composed of consecutive voxels that filled the outer surfaces and the interior of the stem and large branches, and a cloud of voxels equivalent to small branches that were discretely scattered in mainly the upper part of the target. Using this model, we estimated the woody material volume by counting the number of voxels in each part and multiplying the number of voxels by the unit voxel volume (0.13 cm?). The percentage error of the volume of the stem and the part of a large branch was 0.5%. The estimation error of a certain part of the small branches was 34.0%.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58708
Title: Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data.
Author: A. Ramoelo, A K Skidmore, M A Cho, R Mathieu, I M A, Heitkonig, N Dudeni-Tlhone, M Schlerf, H H T Prins.
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier B. V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 82, pp. 27-40 (2013)
Subject: ISPRS Journal of Photogrammetry & Remote Sensing
Keywords: In situ hyperspectral remote sensing, Ecosystem, Partial least square regression, Radial basis neutral network, Nitrogen concentrations, Phosphorus concentrations.
Abstract: Grass nitrogen (N) and phosphorous (P) concentrations are direct indicators of rangeland quality and provide imperative information for sound management of wildlife and lifestock. It is challenging to estimate grass N and P concentrations using remote sensing in the savanna ecosystems. These areas area diverse and heterogenous in soil and plant moisture, soil nutrients, grazing pressures, and human activities. The objective of the study is to test the performance of non-linear partial least squares regression (PLSR) for predicting grass N and P concentrations through integrating in situ hyperspectral remote sensing and environment variables (climatic, edaphic, and topographic). Data were collected along a land use gradient environment in the greater Kruger National Park region. The data consisted of: (i) in situ-measured hyperspectral spectra, (ii) environmental variables and measured grass N and P concentrations. The hyperspectral varialbles included published starch, N and protien spectral absorption features, red edge position, narrow-band indicies such as simple ratio(SR) and normalised difference vegetation index (NDVI). The results of the non-linear PLSR were compared to those conventional linear PLSR. Using non-linear PLSR, integrating in situ hyperspectral and environmental variables yielded highest grass N and P estimations accuracy (R?=0.81, root mean square error (RMSE)=0.08, and R?=0.80, RMSE=0.03, respectively) as compared to using remote sensing variables only, conventional PLSR. The study demonstrates the importance of an integrated modelling approach for estimating grass quality which is crucial effort towards effective management and planning of protected and communal savanna ecosystems.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58707
Title: Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z)
Author: Dimitri Lague, Nicolas Brodu, Jerome Leroux
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier B. V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 82, pp. 10-26 (2013)
Subject: ISPRS Journal of Photogrammetry & Remote Sensing
Keywords: Terrestrial laser scanner, Point cloud, 3D change detection, Surface roughnes, Self-affinity, Geomorphology.
Abstract: Surveying techniques such as terrestrial laser scanner have recently been used to measure surface changes via 3D point cloud (PC) comparison. Two types of approaches have been pursued : 3D tracking of homologus parts of the surface to compute a displacement field, and distance calculation between two point clouds when homologous parts cannot be defined. This study deals with the second approach typical of natural surface altered by erosion, sedimentation or vegetation between surveys. Current comparison methods are based on a closest point distance or require at least one of the PC to be meshed with severe limitations when surfaces present roughness elements at all scales. To solve these issues, we introduce a new algorithm performing a direct comparison of point clouds in 3D. The method has two steps (1) surface normal estimation and orientation in 3D at a scale consistent with the local surface roughness (2) measurement of the mean surface change along the normal direction with explicit calculation of a local confidence interval. Comparison with existing methods demonstrates the higher accuracy of our approach, as well as an easier workflow due to the absence of surface meshing or Digital Elevation Mode (DEM) generation. Application of the method in a rapidly eroding, meandering bedrock river (Rangtikei River canyon) illustrates its ability to handle 3D differences in complex situations (flat and vertical surfaces on the same scene), to reduce uncertainity related to point cloud roughness by local averaging and to generate 3D maps of uncertainity levels. We also demonstrate that for high precision survey scanner, the total error budget on change detection is dominated by the point clouds registration error and the surface roughness. Combined with mm-range local georeferencing of the point clouds, levels of detection down to 6 mm (defined at 95% confidence) can be routinely attained in situ over ranges of 50 m. We provide evidence for self-affine behaviour of different surfaces. We show how this impacts the calculation of normal vectors and demonstrate the scaling behaviour of different surfaces. We show how this impacts the calculation of normal vectors and demonstrate the scaling behaviour of the level of change detection. The algorithm has been implemented in a freely available open source software package. It operates in complex 3D cases and can also be used as a simpler and more robust alternative to DEM differencing for the 2D cases.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58706
Title: A multiresolution hierarchical classification algorithm for filtering airborne LiDAR data.
Author: Chuanfa Chen, Yanyan Li, Wei Li, Honglei Dai.
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier B. V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry & Remote Sensing Vol. 82, pp. (2013)
Subject: ISPRS Journal of Photogrammetry & Remote Sensing
Keywords: LIDAR, Filtering, Thin plate spine, Accuracy.
Abstract: We presented a multiresolution hierarchical classification (MHC) algorithm for differentiating ground from non-ground LiDAR point cloud based on point residuals from the interpolated raster surface. MHC includes three levels of hierarchy, with the simultaneous increase of cell resolution and residual threshold from the low to the high level of the hierarchy. At each level, the surface is iteratively interpolated towards the ground using thin plate spline (TPS) until no ground points are classified, and the classified ground points are used to update the surface in the next iteration. 15 groups of benchmark dataset, provided by International Society for Photogrammetry and Remote Sensing (ISPRS) commision, were used to compare the performance of MHC with the average total error and average Cohen ' s kappa coefficient of 4.11% and 86.27% performs better than all other filtering methods.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58705
Title: Assessing Lidar Accuracy with Hexagonal Retro-Reflective Targets
Author: Roberto Canavosio-Zuzelski, James Hogarty, Craig Rodarmel, Mark Lee, Aaron Braun.
Editor: Russell G Congalton
Year: 2013
Publisher: ESRI
Source: Centre for Ecological Sciences
Reference: Photogrammetric Engineering & Remote Sensing Vol. 79(no. 7), pp. 663-670 (2013)
Subject: Photogrammetric Engineering & Remote Sensing
Keywords: Lidar Accuracy, Hexagonal Retro-Reflective Targets, terrain information.
Abstract: Airborne lidar systems have the potential to produce extremely accurate terrain information at high spatial densities. However, to meet stringent accuracy requirements and minimize systematic errors, proper calibration of the lidar system is required. "Boresighting" is a technique used to correct for some of the these systematic errors and improve the spatial alignment of lidar passes. One challenge with boresighting is the mensuration accuracy to which a known point can be located in overlapping low density lidar strips. To address this issue, a raised Hexagonal Retro-Reflective Lidar ground Target (HRRT) is introduced. The target was optimized for precise mensuration at low point densities. The mensuration model is based on a least squares hexagon fitting approach and is proven to produce mensuation accuracies of 5cm horizontal and 4cm vertical (1-sigma) at ~2 pts/m?. To demonstrate a practical application, the HRRT ' s are used as tie points in a rigorous boresight adjustment to compute lidar strip misalignment parameters (roll, pitch, heading, and range bias). The adjustment results show that accurate boresight parameters are recovered along with their associated uncertainities.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58704
Title: Utility of a Wavelet Transform for LAI Estimation Using Hyperspectral Data.
Author: Asim Banskota, Randoiph H Wynne, Shawn P Serbin, Nilam Kayastha, Valerie A Thomas, Philip A Townsend.
Editor: Russell G Congalton
Year: 2013
Publisher: ESRI
Source: Centre for Ecological Sciences
Reference: Photogrammetric Engineering & Remote Sensing Vol. 79(no. 7), pp. 653-662 (2013)
Subject: Photogrammetric Engineering & Remote Sensing
Keywords: Utility, Wavelet Transform, LAI Estimation, Hyperspectral Data.
Abstract: We employed the discrete wavelet transform to reflectance spectra obtained from hyperspectral data to improve estimation of LAI in temperate forests. We estimated LAI for 32 plots across a range of forest types in Wisconsin using hemispherical photography. Plot spectra were extracted from AVIRIS data and transformed into wavelet features using the Haar wavelet. Separately, subsets of spectral bands and the Haar features selected by a genetic algorithm were used as independent variables in linear regressions. Models using wavelet coefficients explained the most variance for both broadleaf plots (R?=0.90 for wavelet features versus R?=0.80 for spectral bands) and all plots independent of forest type (R?= 0.79 for wavelet features vs. R?=0.58 for spectral bands). The forest-type specific models were better than the models using all plots combined. Overall, wavelet features appear superior to band reflectances alone for estimating temperate forest LAI using hyperspectral data.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58703
Title: Land Subsidence Characteristics in Banding City, Indonesia as Revealed by Spaceborne Geodetic Techniques and Hydrogeological Observations.
Author: R S Chatterjee, Moh. Fifik Syafiuddin, Hasanuddin Z Abidin
Editor: Russell G Congalton
Year: 2013
Publisher: ESRI
Source: Centre for Ecological Sciences
Reference: Photogrammetric Engineering & Remote Sensing Vol. 79(no. 7), pp. 639-652 (2013)
Subject: Photogrammetric Engineering & Remote Sensing
Keywords: Land Subsidence Characteristics, Banding City, Indonesia, Spaceborne Geodetic Techniques, Hydrogeological Observations.
Abstract: Bandung, the capital city of West Java Province, Indonesia has been subsiding as reported by a series of Global Positioning Systems (GPS) observations and field evidence. In this work, an integrated satellite-based approach has been adopted using DINSAR and GPS observations to spatially delineate the subsidence-affected areas and cross-validate the subsidence rates using two collateral geodetic techniques. Multi-frequency DINSAR using C- and L-band SAR data facilitates to monitor land subsidence scenario in totality. C-band DINSAR has been found particularly useful to identify slowly subsiding areas with a sub-centimeter level of precision. Furthermore, the initial hypothesis that land subsidence in Bandung Basin has been occuring due to excessive ground water withdrawal has been established in this study. A predictive modelling approach has been adopted to estimate the rates of potential subsidence due to elastic and inelastic deformations of the aquifer and overlying strata in response to the lowering of groundwater level.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58702
Title: Geolocation Algorithm for Earth Observation Sensors Onboard the International Space Station.
Author: Changyong Dou, Xiaodong Zhang, Hojin Kim, Jaganathan Ranganathan, Doug Olsen, Huadong Guo.
Editor: Russell G Congalton
Year: 2013
Publisher: ESRI
Source: Centre for Ecological Sciences
Reference: Photogrammetric Engineering & Remote Sensing Vol. 79(no. 7), pp. 625-638 (2013)
Subject: Photogrammetric Engineering & Remote Sensing
Keywords: Geolocation Algorithm, Earth Obervation Sensors, Interanational Space station.
Abstract: As a near orbit space platform, the International Space Station (ISS) has been increasingly used for Earth observing applications. This paper presents a quaternion-based forward geolocation algorithm for Earth observing sensors onboard the ISS. The input parameters include the orbital state and attitude information of the ISS and the look vector of the sensor . The proposed algorithm agrees with the commercial navigation product. Satellite Tool Kit, within 0.5 m in ideal situations. The inherent uncertainities in ISS attitude and state determinations, and the International Space Station Agriculture Camera (ISSAC) tilting angle were estimated to introduce an error less than 800m. However, the actual geolocation error evaluated using the images obtained by ISSAC is roughly 4km, much greater than the inherent uncertainity and mainly due to (a) delay caused by the Windows operating system in acquiring images, and (b) the misalignment of the ISAAC sensor coordinate system with the ISS body-fixed coordinate system. A preliminary cal/val process using the Google Earth as reference was performed to quantify these two errors, the correction of which improved the geolocation accuracy to 500 m, well within the inherent uncertainity.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58701
Title: The Effects of Data Selection and Thematic Detail on the Accuracy of High Spatial Resolution Wetland Classifications.
Author: Joseph F Knight, Bryan P Tolcser, Jennifer M Corcoran, Lian P Rampi.
Editor: Russell G Congalton
Year: 2013
Publisher: ESRI
Source: Centre for Ecological Sciences
Reference: Photogrammetric Engineering & Remote Sensing Vol. 79(no. 7), pp. 613-625 (2013)
Subject: Photogrammetric Engineering & Remote Sensing
Keywords: Data Selection, Thematic Detail, Accuracy, High Spatial Resolution, Wetland Classifications.
Abstract: Accurate wetland maps are of critical importance for preserving the ecosystem functions provided by these valuable landscape elements. Though extensive research into wetland mapping methods using remotely sensed data exists, questions remain as to the effects of data type and classification accuracy when high spatial resolution data is used. The goal of this research was to examine the effects on wetland mapping accuracy of varying input datasets and thematic detail in two physiographically different study areas using a decision tree classifier. The results indicate that: topographic data and derivatives significantly increase mapping accuracy over optical imagery alone, the source of the elevation data and the type of topographic derivatives used were not major factors, the inclusion of radar and leaf-off imagery did not improve mapping accuracy, and increasing thematic detail resulted in significantly lower mapping accuracies i.e., paricularly in more diverse wetland areas.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58700
Title: Stitching and Processing Gnomonic Projections for Close-Range Photogrammetry.
Author: Lulgi Barazzetti, Mattia Previtalli, Marco Scaioni.
Editor: Russell G Congalton
Year: 2013
Publisher: ESRI
Source: Centre for Ecological Sciences
Reference: Photogrammetric Engineering & Remote Sensing Vol. 79(no. 6), pp. 573-582 (2013)
Subject: Photogrammetric Engineering & Remote Sensing
Keywords: Stitching, Processing, Gnomonic Projections, Close-Range Photgrammetry.
Abstract: This paper presents a 3D reconstruction methodology based on gnomonic projections generated from multiple central perspectives. The method can be useful when traditional images are insufficient to capture fine portions of objects with an adequate level in detail, especially in close-range photogrammetry. The aim of this paper is to prove that gnomonic projections are powerfull tools to recover small details that cannot be reconstructed from standard images. The generation of the gnomonic projections are discussed, as well as the methodology to compute camera parameters, orient multiple projections, and finally extract textured 3D models or orthophotos. To verify the corectness of the methodology, some comparisons with sets of independent checkpoints were carried out. The accuracy assessment confirmed the correctness of the mathematical approach and underlined how gnomonic projections are a valid alternative to standard images. The method itself has been already used in surveys for cultural heritage documentation.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58699
Title: A Flexible Method for Zoom Lens Calibration and Modelling Using a Planar Checkerboard.
Author: Bo Wu, Han Hu, Qing Zhu, Yeting Zhang.
Editor: Russell G Congalton
Year: 2013
Publisher: ESRI
Source: Centre for Ecological Sciences
Reference: Photogrammetric Engineering & Remote Sensing Vol. 79(no. 6), pp. 555-572 (2013)
Subject: Photogrammetric Engineering & Remote Sensing
Keywords: Flexible method, Zoom Lens Calibration, Modelling, Planar checkboard.
Abstract: This paper presents a flexible method for zoom lens calibration and modelling using a planar checkerboard. The method includes the following four steps. First, the principal point of the zoom-lens camera is determined by a focus-of-expansion approach. Second, the influences of focus changes on the principal distance are modeled by a scale parameter. Third, checkerboard images taken at varying object distances with convergent image geometry are used for camera calibration. Finally, The variations of the calibration parameters with respect to various zoom and focus settings are modeled using polynomials. Three different types of lens are examined in this study. Experimental analyses show that high precision calibration results can be expected from the developed approach. The relative measurement accuracy (accuracy normalized with object distance) using the calibrated zoom-lens camera model ranges from 1:5 000 to 1:2500. The developed method is of significance to facililtitate the use of zoom-lens camera systems in various applications such as robotic exploration hazard monitoring, traffic monitoring, traffic monitoring and security surveilsn
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None