ID: 59448
Title: Evaluation of data fusion and image segmentation in earth observation based rapid mapping workflows.
Author: Chandi Witharana, Daniel L. Civco, Thomas H. Meyer
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 1-18 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Image fusion; Image segmentation; Humanitarian information; Rapid mapping; GEOBIA; VHSR images.
Abstract: This paper is an exploratory study, which aimed to discover the synergies of data fusion and image segmentation in the context of EO-based rapid mapping workflows. Our approach pillared on the geographic object-based image analysis (GEOBIA) focusing on multiscale, internally-displaced persons ' (IDP) camp information extraction from very high spatial resolution (VHSR) images. We applied twelve pansharpening algorithms to two subsets of a GeoEye-1 image scene that was taken over a former war-induced ephemeral settlement in Sri Lanka. A multidimensional assessment was employed to benchmark pansharpening algorithms with respect to their spectral and spatial fidelity. The multiresolution segmentation (MRS) algorithm of the eCognition Developer software served as the key algorithm in the segmentation process. The first study site was used for comparing segmentation results produced from the twelve fused products at a series of scale, shape, and compactness settings of the MRS algorithm. The segmentation quality and optimum parameter settings of the MRS algorithm were estimated by using empirical discrepancy measures. Non-parametric statistical tests were used to compare the quality of image object candidates, which were derived from the twelve pansharpened products. A wall-to-wall classification was performed based on a support vector machine (SVM) classifier to classify image objects candidates of the fused images. The second site simulated a more realistic crisis information extraction scenario where the domain expertise is crucial in segmentation and classification. We compared segmentation and classification results of the original images (non-fused) and twelve fused images to understand the efficacy of data fusion. We have shown that the GEOBIA has the ability to create meaningful image objects during the segmentation process by compensating the fused image ' s spectral distortions with the high-frequency information content that has been injected during fusion. Our findings further questioned the necessity of the data fusion step in rapid mapping context. Bypassing time-intensive data fusion helps to actuate EO-based rapid mapping workflows. We, however, emphasize the fact that data fusion is not limited to VHSR image data but expands over many different combinations of multi-date, multi-sensor EO-data. Thus, further research is needed to understand the synergies of data fusion and image segmentation with respect to multi-date, multi-sensor fusion scenarios and extrapolate our findings to other remote sensing application domains beyond EO-based crisis information retrieval.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59447
Title: Assessing canopy PRI from airborne imagery to map water stress in maize
Author: M. Rossini, F. Fava, S. Cogliati, M. Meroni, A. Marchesi, d, C. Panigada, C. Giardino, L. Busetto, M. Migliavacca, S. Amaducci, R. Colombo
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 86, 168-177 (2013)
Subject: Photogrammetry and Remote Sensing.
Keywords: Hyperspectral; Vegetation; Monitoring; Aerial; Crop
Abstract: This paper presents a method for mapping water stress in a maize field using hyperspectral remote sensing imagery. An airborne survey using AISA (Specim, Finland) was performed in July 2008 over an experimental farm in Italy. Hyperspectral data were acquired over a maize field with three different irrigation regimes. An intensive field campaign was also conducted concurrently with imagery acquisition to measure relative leaf water content (RWC), active chlorophyll fluorescence (?F/F?m), leaf temperature (T l) and Leaf Area Index (LAI). The analysis of the field data showed that at the time of the airborne overpass the maize plots with irrigation deficits were experiencing a moderate water stress, affecting the plant physiological status (?F/F?m, difference between T l and air temperature (T air), and RWC) but not the canopy structure (LAI). Among the different Vegetation Indices (VIs) computed from the airborne imagery the Photochemical Reflectance Index computed using the reflectance at 570 nm as the reference band (PRI570) showed the strongest relationships with ?F /F?m (r2 = 0.76), Tl ? Tair (r2 = 0.82) and RWC (r2 = 0.64) and the red-edge Chlorophyll Index (CIred-edge) with LAI (r2 = 0.64). Thus PRI has been proven to be related to water stress at early stages, before structural changes occurred.
A method based on an ordinal logit regression model was proposed to map water stress classes based on airborne hyperspectral imagery. PRI570 showed the highest performances when fitted against water stress classes, identified by the irrigation amounts applied in the field, and was therefore used to map water stress in the maize field. This study proves the feasibility of mapping stress classes using hyperspectral indices and demonstrates the potential applicability of remote sensing data in precision agriculture for optimizing irrigation management.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59446
Title: Gaussian processes uncertainty estimates in experimental Sentinel-2 LAI and leaf chlorophyll content retrieval
Author: Jochem Verrelst, Juan Pablo Rivera, Jos? Moreno, Gustavo Camps-Valls
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 86, 157-167 (2013)
Subject: Photogrammetry and Remote Sensing.
Keywords: Uncertainty estimates; Gaussian processes regression; Biophysical parameters; Sentinel-2; Chlorophyll content;
Abstract: ESA ' s upcoming Sentinel-2 (S2) Multispectral Instrument (MSI) foresees to provide continuity to land monitoring services by relying on optical payload with visible, near infrared and shortwave infrared sensors with high spectral, spatial and temporal resolution. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods, which ideally should provide uncertainty intervals for the predictions. Statistical learning regression algorithms are powerful candidats for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting. In this paper, we focus on a new emerging technique in the field of Bayesian nonparametric modeling. We exploit Gaussian process regression (GPR) for retrieval, which is an accurate method that also provides uncertainty intervals along with the mean estimates. This distinct feature is not shared by other machine learning approaches. In view of implementing the regressor into operational monitoring applications, here the portability of locally trained GPR models was evaluated. Experimental data came from the ESA-led field campaign SPARC (Barrax, Spain). For various simulated S2 configurations (S2-10m, S2-20m and S2-60m) two important biophysical parameters were estimated: leaf chlorophyll content (LCC) and leaf area index (LAI). Local evaluation of an extended training dataset with more variation over bare soil sites led to improved LCC and LAI mapping with reduced uncertainties. GPR reached the 10% precision required by end users, with for LCC a NRMSE of 3.5-9.2% (r2: 0.95-0.99) and for LAI a NRMSE of 6.5-7.3% (r2: 0.95-0.96). The developed GPR models were subsequently applied to simulated Sentinel images over various sites. The associated uncertainty maps proved to be a good indicator for evaluating the robustness of the retrieval performance. The generally low uncertainty intervals over vegetated surfaces suggest that the locally trained GPR models are portable to other sites and conditions.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59445
Title: Photogrammetric modeling of the relative orientation in underwater environments
Author: Gili Telem, Sagi Filin.
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 86, 150-156 (2013)
Subject: Photogrammetry and Remote Sensing.
Keywords: Shurbland; Regression tree; Continuous field; Landsat; WorldView-2
Abstract: Underwater photogrammetry provides an efficient means for documentation of environments which are complex and have limited accessibility. Yet the establishment of reference control networks in such settings is oftentimes difficult. In this regard, use of the coplanarity condition, which requires neither knowledge of object space coordinates nor setting a reference control network, seems to be an attractive solution. However, the coplanarity relation does not hold in such environments because of the refraction effect, and methods that have been proposed thus far for geometrical modeling of its effect require knowledge of object-space quantities. Thus, this paper proposes a geometrically-driven approach which fulfills the coplanarity condition and thereby requires no knowledge of object space data. Such an approach may prove useful not only for object space reconstruction but also as a preparatory step for application of bundle block adjustment and for outlier detection. All are key features in photogrammetric practices. Results show that no unique setup is needed for estimating the relative orientation parameters using the model and that high levels of accuracy can be achieved.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59444
Title: An approach for characterizing the distribution of shrubland ecosystem components as continuous fields as part of NLCD.
Author: George Xian, Collin Homer, Debbie Meyer, Brian Granneman.
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 86, 136-149 (2013)
Subject: Photogrammetry and Remote Sensing.
Keywords: Shurbland; Regression tree; Continuous field; Landsat; WorldView-2
Abstract: Characterizing and quantifying distributions of shrubland ecosystem components is one of the major challenges for monitoring shrubland vegetation cover change across the United States. A new approach has been developed to quantify shrubland components as fractional products within National Land Cover Database (NLCD). This approach uses remote sensing data and regression tree models to estimate the fractional cover of shrubland ecosystem components. The approach consists of three major steps: field data collection, high resolution estimates of shrubland ecosystem components using WorldView-2 imagery, and coarse resolution estimates of these components across larger areas using Landsat imagery. This research seeks to explore this method to quantify shrubland ecosystem components as continuous fields in regions that contain wide-ranging shrubland ecosystems. Fractional cover of four shrubland ecosystem components, including bare ground, herbaceous, litter, and shrub, as well as shrub heights, were delineated in three ecological regions in Arizona, Florida, and Texas. Results show that estimates for most components have relatively small normalized root mean square errors and significant correlations with validation data in both Arizona and Texas. The distribution patterns of shrub height also show relatively high accuracies in these two areas. The fractional cover estimates of shrubland components, except for litter, are not well represented in the Florida site. The research results suggest that this method provides good potential to effectively characterize shrubland ecosystem conditions over perennial shrubland although it is less effective in transitional shrubland. The fractional cover of shrub components as continuous elements could offer valuable information to quantify biomass and help improve thematic land cover classification in arid and semiarid areas.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59443
Title: The potential of linear discriminative Laplacian eigenmaps dimensionality reduction in polarimetric SAR classification for agricultural areas.
Author: Lei Shi, Lefei Zhang, Lingli Zhao, Jie Yang, PingXiang Li, Liangpei Zhang.
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 86, 124-135 (2013)
Subject: Photogrammetry and Remote Sensing.
Keywords: Polarimetric synthetic aperture radar; Supervised classification; Dimensionality reduction
Abstract: In this paper, the linear discriminative Laplacian eigenmaps (LDLE) dimensionality reduction (DR) algorithm is introduced to C-band polarimetric synthetic aperture radar (PolSAR) agricultural classification. A collection of homogenous areas of the same crop class usually presents physical parameter variation, such as the biomass and soil moisture. Furthermore, the local incidence angle also impacts a lot on the same crop category when the vegetation layer is penetrable with C-band radar. We name this phenomenon as the ?observed variation of the same category? (OVSC). The most common PolSAR features, e.g., the Freeman-Durden and Cloude-Pottier decompositions, show an inadequate performance with OVSC. In our research, more than 40 coherent and incoherent PolSAR decomposition models are stacked into the high-dimensionality feature cube to describe the various physical parameters. The LDLE algorithm is then performed on the observed feature cube, with the aim of simultaneously pushing the local samples of the same category closer to each other, as well as maximizing the distance between local samples of different categories in the learnt subspace. Finally, the classification result is obtained by nearest neighbor (NN) or Wishart classification in the reduced feature space. In the simulation experiment, eight crop blocks are picked to generate a test patch from the 1991 Airborne Synthetic Aperture Radar (AIRSAR) C-band fully polarimetric data from of Flevoland test site. Locality preserving projections (LPP) and principal component analysis (PCA) are then utilized to evaluate the DR results of the proposed method. The classification results show that LDLE can distinguish the influence of the physical parameters and achieve a 99% overall accuracy, which is better than LPP (97%), PCA (88%), NN (89%), and Wishart (88%). In the real data experiment, the Chinese Hailaer nationalized farm RadarSat2 PolSAR test set is used, and the classification accuracy is around 94%, which is again better than LPP (90%), PCA (88%), NN (89%), and Wishart (85%). Both experiments suggest that the LDLE algorithm is an effective way of relieving the OVSC phenomenon.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59442
Title: Automatic detection of a one dimensional ranging pole for robust external camera calibration in mobile mapping.
Author: Koen Douterloigne, Werner Goeman, Sidharta Gautama, Wilfried Philips.
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 86, 111-123 (2013)
Subject: Photogrammetry and Remote Sensing.
Keywords: Mobile; Mapping; Automation; Camera; Calibration; Registration.
Abstract: A mobile mapping system (MMS) is the answer of the geoinformation community to the exponentially growing demand for various geospatial data with increasingly higher accuracies, captured by multiple sensors. As the mobile mapping technology is pushed to explore its use for various applications on water, rail, or road, the need emerges to have an external sensor calibration procedure that is portable, fast and easy to perform. This way, sensors can be mounted and demounted depending on the application requirements without the need for time consuming calibration procedures. A new methodology is presented to provide a high quality external calibration of cameras which is automatic, robust and fool proof. The method uses a portable, standard ranging pole which needs to be positioned on a known ground control point. While the literature focuses on solving the absolute orientation problem of the calibration, an automatic method to detect the calibration object is missing. Here, we present a mutual information based image registration technique for automatic sub-pixel localization of the ranging pole under realistic outdoor conditions. We include several robust error detection rules to allow the calibration to run without human intervention, giving as little outliers as possible, to ensure a correct calibration. Several tests are performed under various lighting and noise conditions which prove the methodology ' s robustness.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59441
Title: The use of single-date MODIS imagery for estimating large-scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques.
Author: Chengbin Deng, Changshan Wu.
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 86, 100-110 (2013)
Subject: Photogrammetry and Remote Sensing.
Keywords: Impervious surface, MODIS, Random Forests, Regression tree, Spectral mixture analysis, V-I-S model.
Abstract: Urban impervious surface information is essential for urban and environmental applications at the regional/national scales. As a popular image processing technique, spectral mixture analysis (SMA) has rarely been applied to coarse resolution imagery due to the difficulty of deriving endmember spectra using traditional endmember selection methods, particularly within heterogenous urban environments. To address this problem, we derived end member signatures through a least squares solution (LSS) technique with known abundances of sample pixels, and integrated these endmembers signatures into SMA for mapping large-scale impervious surface fraction. In addition, with the same sample set, we carried out objective comparative analyses among SMA (i.e fully constrained and unconstrained SMA) and machine learning (i.e. Cubist regression tree Random Forests) techniques. Analysis of results suggests three major conclusions. First, with the extrapolated endmember spectra from stratified random training samples, the SMA approaches performed relatively well, as indicated by small MAE values. Second, Random Forests yields more reliable results than Cubist regression tree, and its accuracy is improved with increased sample sizes. Finally, comparative analyses suggest a tentative guide for selecting an optimal approach for large-scale fractional imperviousness estimation: unconstrained SMA might be a favorable option with a small number of samples, while Random Forests might be preferred if a large number of samples are available.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59440
Title: Camera derived vegetation greenness index as proxy for gross primary production in a low Arctic wetland area.
Author: Andreas Westergaard-Nielsen, Magnus Lund, Birger Ulf Hansen, Mikkel Peter Tamstorf.
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 86, 89-99 (2013)
Subject: Photogrammetry and Remote Sensing.
Keywords: Low Arctic; Digital camera; Vegetation index; Carbon; Eddy covariance; Gross primary production.
Abstract: The Arctic is experiencing disproportionate warming relative to the global average, and the Arctic ecosystems are as a result undergoing considerable changes. Continued monitoring of ecosystem productivity and phenology across temporal and spatial scales is a central part of assessing the magnitude of these changes. This study investigates the ability to use automatic digital camera images (DCIs) as proxy data for gross primary production (GPP) in a complex low Arctic wetland site. Vegetation greenness computed from DCIs was found to correlate significantly (R2 = 0.62, p < 0.001) with a normalized difference vegetation index (NDVI) product derived from the WorldView-2 satellite. An object-based classification based on a bi-temporal image composite was used to classify the study area into heath, copse, fen, and bedrock. Temporal evolution of vegetation greenness was evaluated and modeled with double sigmoid functions for each plant community. GPP at light saturation modeled from eddy covariance (EC) flux measurements were found to correlate significantly with vegetation greenness for all plant communities in the studied year (i.e., 2010), and the highest correlation was found between modeled fen greenness and GPP (R2 = 0.85, p < 0.001). Finally, greenness computed within modeled EC footprints were used to evaluate the influence of individual plant communities on the flux measurements. The study concludes that digital cameras may be used as a cost-effective proxy for potential GPP in remote Arctic regions.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59439
Title: Satellite-based investigation of flood-affected rice cultivation areas in Chao Phraya River Delta, Thailand.
Author: N.T. Son, C.F. Chen, C.R. Chen, L.Y. Chang.
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 86, 77-88 (2013)
Subject: Photogrammetry and Remote Sensing.
Keywords: MODIS; Flood-affected areas; Rice agriculture; Change detection.
Abstract: The occurrence of catastrophic floods in Thailand in 2011 caused significant damage to rice agriculture. This study investigated flood-affected rice cultivation areas in the Chao Phraya River Delta (CRD) rice bowl, Thailand using time-series moderate resolution imaging spectroradiometer (MODIS) data. The data were processed for 2008 (normal flood year) and 2011, comprising four main steps: (1) data pre-processing to construct time-series MODIS vegetation indices (VIs), to filter noise from the time-series VIs by the empirical mode decomposition (EMD), and to mask out non-agricultural areas in respect to water-related cropping areas; (2) flood-affected area classification using the unsupervised linear mixture model (ULMM); (3) rice crop classification using the support vector machines (SVM); and (4) accuracy assessment of flood and rice crop mapping results. The comparisons between the flood mapping results and the ground reference data indicated an overall accuracy of 97.9% and Kappa coefficient of 0.62 achieved for 2008, and 95.7% and 0.77 for 2011, respectively. These results were reaffirmed by close agreement (R2 > 0.8) between comparisons of the two datasets at the provincial level. The crop mapping results compared with the ground reference data revealed that the overall accuracies and Kappa coefficients obtained for 2008 were 88.5% and 0.82, and for 2011 were 84.1% and 0.76, respectively. A strong correlation was also found between MODIS-derived rice area and rice area statistics at the provincial level (R2 > 0.7). Rice crop maps overlaid on the flood-affected area maps showed that approximately 16.8% of the rice cultivation area was affected by floods in 2011 compared to 4.9% in 2008. A majority of the flood-expanded area was observed for the double-cropped rice (10.5%), probably due to flood-induced effects to the autumn-summer and rainy season crops. Information achieved from this study could be useful for agricultural planners to mitigate possible impacts of floods on rice production.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59438
Title: Bi-cubic interpolation for shift-free pan-sharpening.
Author: Bruno Aiazzi, Stefano Baronti, Massimo Selva, Luciano Alparone.
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 86, 65-76 (2013)
Subject: Photogrammetry and Remote Sensing.
Keywords: Digital filtering; Interpolation; Linear phase; MS scanners; Pan-sharpening; Piecewise local polynomials.
Abstract: Most of pan-sharpening techniques require the re-sampling of the multi-spectral (MS) image for matching the size of the panchromatic (Pan) image, before the geometric details of Pan are injected into the MS image. This operation is usually performed in a separable fashion by means of symmetric digital low-pass filtering kernels with odd lengths that utilize piecewise local polynomials, typically implementing linear or cubic interpolation functions. Conversely, constant, i.e. nearest-neighbour, and quadratic kernels, implementing zero and two degree polynomials, respectively, introduce shifts in the magnified images, that are sub-pixel in the case of interpolation by an even factor, as it is the most usual case. However, in standard satellite systems, the point spread functions (PSF) of the MS and Pan instruments are centered in the middle of each pixel. Hence, commercial MS and Pan data products, whose scale ratio is an even number, are relatively shifted by an odd number of half pixels. Filters of even lengths may be exploited to compensate the half-pixel shifts between the MS and Pan sampling grids. In this paper, it is shown that separable polynomial interpolations of odd degrees are feasible with linear-phase kernels of even lengths. The major benefit is that bi-cubic interpolation, which is known to represent the best trade-off between performances and computational complexity, can be applied to commercial MS + Pan datasets, without the need of performing a further half-pixel registration after interpolation, to align the expanded MS with the Pan image.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59437
Title: Change detection in urban areas by object-based analysis and on-the-fly comparison of multi-view ALS data
Author: Marcus Hebela, Michael Arensa, Uwe Stillab.
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 86, 52-64 (2013)
Subject: Photogrammetry and Remote Sensing.
Keywords: Airborne laser scanning; LiDAR; Change detection; Multi-aspect; Multi-temporal data analysis.
Abstract: The use of helicopters as a sensor platform offers flexible fields of application due to adaptable flying speed at low flight levels. Modern helicopters are equipped with radar altimeters, inertial navigation systems (INS), forward-looking cameras and even laser scanners for automatic obstacle avoidance. If the 3D geometry of the terrain is already available, the analysis of airborne laser scanner (ALS) measurements may also be used for terrain-referenced navigation and change detection. In this paper, we present a framework for on-the-fly comparison of current ALS data to given reference data of an urban area. In contrast to classical difference methods, our approach extends the concept of occupancy grids known from robot mapping. However, it does not blur the measured information onto the grid cells. The proposed change detection method applies the Dempster-Shafer theory to identify conflicting evidence along the laser pulse propagation path. Additional attributes are considered to decide whether detected changes are of man-made origin or occurring due to seasonal effects. The concept of online change detection has been successfully validated in offline experiments with recorded ALS data streams. Results are shown for an urban test site at which multi-view ALS data were acquired at an interval of 1 year.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59436
Title: Development of a geometric model for an all-reflective camera system.
Author: K. Richter, D. Mader , K. Seidl , H.-G. Maas.
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 86, 41-51 (2013)
Subject: Photogrammetry and Remote Sensing.
Keywords: All-reflective camera system; Calibration; Geometric modeling; FEM.
Abstract: Currently in photogrammetry conventional camera optics, which are based on a combination of lenses, are solely used. These systems are also called refractive systems. The usage of refractive systems implies a general drawback for some applications. Due to the chromatic aberration of lenses, i.e. slightly different imaging functions for different spectral bands, a significant loss of image quality and geometric accuracy has to be accepted. This fact is important especially for applications that require imaging a wide spectral range. Conventional cameras are not able to satisfactorily capture the ultraviolet or near infrared spectral range in addition to the visible.
These chromatic aberration problems can be completely be avoided in all-reflective optical systems, i.e. camera objectives which are completely based on mirrors. The paper will briefly describe the developed all-reflective optical systems designed for optical metrology purposes.
A general disadvantage of the design of normal or wide angle all-reflective systems is the asymmetry of the mirror arrangement, which leads to large asymmetric geometric image distortions. These distortions cannot be modeled with standard methods of photogrammetry. Furthermore, the complete system is also more sensitive to local deviations from the ideal mirror surface. Therefore we developed a suitable geometric model, which is adapted to the special case. The model is based on the collinearity condition, extended by a specific additional parameter set optimized with regard to the characteristics of an all-reflective unobscured system. We will show various model variants based on the additional parameter sets of Brown, Ebner and Gr?n as well as Legendre polynomials, Chebyshev polynomials and Fourier series. The paper discusses the potential of these models to correct the distortion of an all-reflective unobscured optical system prototype based on four aspherical mirrors on the basis of test field self-calibration and describes different approaches to consider local deviations from the nominal aspherical mirror surface with the help of the finite elements method.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59435
Title: Automated detection of buildings from single VHR multispectral images using shadow information and graph cuts.
Author: Ali Ozgun Ok
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 86, 21-40 (2013)
Subject: Photogrammetry and Remote Sensing.
Keywords: Building detection; Shadow evidence; Graph cuts; Satellite imagery.
Abstract: In this study, we propose a novel methodology for automated detection of buildings from single very-high-resolution (VHR) multispectral images. The methodology uses the principal evidence of buildings: the shadows that they cast. We model the directional spatial relationship between buildings and their shadows using a recently proposed probabilistic landscape approach. An effective shadow post-processing step is developed to focus on landscapes that belong to building regions. The building regions are detected using an original two-level graph theory approach. In the first level, each shadow region is addressed separately, and building regions are identified via iterative graph cuts designed in two-label partitioning. The final building regions are characterised in a second level in which the previously labelled building regions are subjected to a single-step multi-label graph optimisation performed over the entire image domain. Numerical assessments performed on 16 VHR GeoEye-1 images demonstrate that the proposed approach is highly robust and reliable. A distinctive specialty of the proposed approach is its applicability to buildings with diverse characteristics as well as to VHR images with significantly different illumination properties.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59434
Title: Radargrammetric registration of airborne multi-aspect SAR data of urban areas
Author: Michael Schmitt, Oliver Maksymiuk , Christophe Magnard , Uwe Stilla
Editor: Derek Lichti
Year: 2013
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 86, 11-20 (2013)
Subject: Photogrammetry and Remote Sensing.
Keywords: Synthetic Aperture Radar (SAR), Multi-aspect; Airborne, Registration, Georeferencing, Gauss-Helmert model.
Abstract: In this paper, the registration of decimeter-resolution airborne multi-aspect SAR (MASAR) data of inner city areas by application of the radargrammetric range-Doppler equations is investigated. The geometrical model is adapted to linear flight trajectories and zero-Doppler processed SAR data, whereas the observed trajectory parameters are adjusted using a strict Gauss-Helmert model and known ground control points. The significance of the estimated corrections is examined and the most suitable set of free parameters is determined. Finally, the methodology is applied to real test data of an airborne campaign over the city of Munich, Germany, and the feasability of the proposed radargrammetric registration method is shown.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None