ID: 59455
Title: Fusion of airborne laserscanning point clouds and images for supervised and unsupervised classification scene classification
Author: Markus, Gerke, Jing Xiao.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 78-92 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Visibility, Segmentation, Supervised, Unsupervised, Classification.
Abstract: Automatic urban object detection from airborne remote sensing data is essential to process and efficiently interpret the vast amount of airborne imagery and Laserscanning (ALS) data available today. This paper combines ALS data and airborne imagery to exploit both: the good geometric quality of ALS and the spectral image information to detect the four classes buildings, trees, vegetated ground and sealed ground. A new segmentation approach is introduced which also makes use of geometric and spectral data during classification entity definition. Geometric, textural, low level and mid level image features are assigned to laser points which are quantified into voxels. The segment information is transferred to the voxels and those clusters of voxels form the entity to be classified. Two classification strategies are pursued: a supervised method, using Random Trees and an unsupervised approach, embedded in a Markov Random Field framework and using graph-cuts for energy optimization. A further contribution of the paper concerns the image-based point densification for building roofs which aims to mitigate the accuracy Problems related to large ALS point spacing. Results for the ISPRS benchmark test data show that to rely on color information to separate vegetation from non-vegetation areas does mostly lead to good results, but in particular in shadow areas a confusion between classes might occur. The unsupervised classification strategy is especially sensitive in this respect. As far as the point cloud densification is concerned, we observe similar sensitivity with respect to color which makes some planes to be missed out, or false detections still remain. For planes where the densification is successful we see the expected enhancement of the outline.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59454
Title: Maximum-likelihood estimation for multi-aspect multi-baseline SAR interferometry of urban areas.
Author: Michael Schmitt, Uwe Stilla.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 68-77 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Synthetic Aperture Radar (SAR); Multi-aspect; Multi-baseline; Airborne; SAR interferometry (InSAR); Maximum likelihood estimation.
Abstract: The reconstruction of digital surface models (DSMs) of urban areas from interferometric synthetic aperture radar (SAR) data is a challenging task. In particular the SAR inherent layover and shadowing effects need to be coped with by sophisticated processing strategies. In this paper, a maximum-likelihood estimation procedure for the reconstruction of DSMs from multi-aspect multi-baseline InSAR imagery is proposed. In this framework, redundant as well as contradicting observations are exploited in a statistically optimal way. The presented method, which is especially suited for single-pass SAR interferometers, is examined using test data consisting of experimental airborne millimeterwave SAR imagery. The achievable accuracy is evaluated by comparison to LiDAR-derived reference data. It is shown that the proposed estimation procedure performs better than a comparable non-statistical reconstruction method.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59453
Title: EarthEnv-DEM90: A nearly-global, void-free, multi-scale smoothed, 90m digital elevation model from fused ASTER and SRTM data.
Author: Natalie Robinson, James Regetz, Robert P. Guralnick.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 57-67 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Global Digital Elevation Model (DEM); Void-filling; Multi-scale smoothing; SRTM; ASTER; DEM blending.
Abstract: A variety of DEM products are available to the public at no cost, though all are characterized by trade-offs in spatial coverage, data resolution, and quality. The absence of a high-resolution, high-quality, well-described and vetted, free, global consensus product was the impetus for the creation of a new DEM product described here, ' EarthEnv-DEM90 ' . This new DEM is a compilation dataset constructed via rigorous techniques by which ASTER GDEM2 and CGIAR-CSI v4.1 products were fused into a quality-enhanced, consistent grid of elevation estimates that spans ~91% of the globe. EarthEnv-DEM90 was assembled using methods for seamlessly merging input datasets, thoroughly filling voids, and smoothing data irregularities (e.g. those caused by DEM noise) from the approximated surface. The result is a DEM product in which elevational artifacts are strongly mitigated from the input data fusion zone, substantial voids are filled in the northern-most regions of the globe, and the entire DEM exhibits reduced terrain noise. As important as the final product is a well defined methodology, along with new processing techniques and careful attention to final outputs, that extends the value and usability of the work beyond just this single product. Finally, we outline EarthEnv-DEM90 acquisition instructions and metadata availability, so that researchers can obtain this high-resolution, high-quality, nearly-global new DEM product for the study of wide-ranging global phenomena.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59452
Title: An algorithm for automatic detection of pole-like street furniture objects from Mobile Laser Scanner point clouds.
Author: C. Cabo, C. Ordo?ez, S. Garc?a-Cort?s, J. Mart?nez
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 47-56 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Urban; Simplification; Detection; Laser scanning; Algorithms; Mobile.
Abstract: An algorithm for automatic extraction of pole-like street furniture objects using Mobile Laser Scanner data was developed and tested. The method consists in an initial simplification of the point cloud based on the regular voxelization of the space. The original point cloud is spatially discretized and a version of the point cloud whose amount of data represents 20-30% of the total is created. All the processes are carried out with the reduced version of the data, but the original point cloud is always accessible without any information loss, as each point is linked to its voxel. All the horizontal sections of the voxelized point cloud are analyzed and segmented separately. The two-dimensional fragments compatible with a section of a target pole are selected and grouped. Finally, the three-dimensional voxel representation of the detected pole-like objects is identified and the points from the original point cloud belonging to each pole-like object are extracted.
The algorithm can be used with data from any Mobile Laser Scanning system, as it transforms the original point cloud and fits it into a regular grid, thus avoiding irregularities produced due to point density differences within the point cloud.
The algorithm was tested in four test sites with different slopes and street shapes and features. All the target pole-like objects were detected, with the only exception of those severely occluded by large objects and some others which were either attached or too close to certain features.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59451
Title: Integrating environmental variables and WorldView-2 image data to improve the prediction and mapping of Thaumastocoris peregrinus (bronze bug) damage in plantation forests.
Author: Zakariyyaa Qumar, Onisimo Mutanga.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 39-46 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Thaumastocoris peregrinus, Environmental variables, Vegetation indices, WorldView-2 imagery, Plantation health monitoring.
Abstract: This study integrated environmental variables together with high spectral resolution WorldView-2 imagery to detect and map Thaumastocoris peregrinus damage in Eucalypt plantation forests in KwaZulu-Natal, South Africa. The WorldView-2 bands, vegetation indices and environmental variables were entered separately into PLS regression models to predict T. peregrinus damage. The datasets were then integrated to test the collective strength in predicting T. peregrinus damage. Important variables were identified by variable importance (VIP) scores and were re-entered into a PLS regression model. The VIP model was then extrapolated to map the severity of damage and predicted T. peregrinus damage with an R2 value of 0.71 and a RMSE of 3.26% on an independent test dataset. The red edge and near-infrared bands of the WorldView-2 sensor together with the temperature dataset were identified as important variables in predicting T. peregrinus damage. The results indicate the potential of integrating WorldView-2 data and environmental variables to improve the mapping and monitoring of insect outbreaks in plantation forests. The result is critical for plantation health monitoring using a new sensor which contains important vegetation wavelengths.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59450
Title: Deriving leaf mass per area (LMA) from foliar reflectance across a variety of plant species using continuous wavelet analysis.
Author: Tao Cheng, Benoit Rivard, Arturo G. S?nchez-Azofeifa, Jean-Baptiste F?ret, St?phane Jacquemoud, Susan L. Ustin.
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 28-38 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Leaf mass per area; Dry matter content; Specific leaf area; PROSPECT model; Remote sensing; Wavelet analysis.
Abstract: Leaf mass per area (LMA), the ratio of leaf dry mass to leaf area, is a trait of central importance to the understanding of plant light capture and carbon gain. It can be estimated from leaf reflectance spectroscopy in the infrared region, by making use of information about the absorption features of dry matter. This study reports on the application of continuous wavelet analysis (CWA) to the estimation of LMA across a wide range of plant species. We compiled a large database of leaf reflectance spectra acquired within the framework of three independent measurement campaigns (ANGERS, LOPEX and PANAMA) and generated a simulated database using the PROSPECT leaf optical properties model. CWA was applied to the measured and simulated databases to extract wavelet features that correlate with LMA. These features were assessed in terms of predictive capability and robustness while transferring predictive models from the simulated database to the measured database. The assessment was also conducted with two existing spectral indices,namely the Normalized Dry Matter Index (NDMI) and the Normalized Difference index for LMA (NDLMA).
Five common wavelet features were determined from the two databases, which showed significant correlations with LMA (R2: 0.51-0.82, p < 0.0001). The best robustness (R2 = 0.74, RMSE = 18.97 g/m2 and Bias = 0.12 g/m2) was obtained using a combination of two low-scale features (1639 nm, scale 4) and (2133 nm, scale 5), the first being predominantly important. The transferability of the wavelet-based predictive model to the whole measured database was either better than or comparable to those based on spectral indices. Additionally, only the wavelet-based model showed consistent predictive capabilities among the three measured data sets. In comparison, the models based on spectral indices were sensitive to site-specific data sets. Integrating the NDLMA spectral index and the two robust wavelet features improved the LMA prediction. One of the bands used by this spectral index, 1368 nm, was located in a strong atmospheric water absorption region and replacing it with the next available band (1340 nm) led to lower predictive accuracies. However, the two wavelet features were not affected by data quality in the atmospheric absorption regions and therefore showed potential for canopy-level investigations. The wavelet approach provides a different perspective into spectral responses to LMA variation than the traditional spectral indices and holds greater promise for implementation with airborne or spaceborne imaging spectroscopy data for mapping canopy foliar dry biomass.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59449
Title: Assessment of the image misregistration effects on object-based change detection
Author: Gang Chen, Kaiguang Zhao, Ryan Powers
Editor: Derek Lichti
Year: 2014
Publisher: Elsevier B V.
Source: Centre for Ecological Sciences
Reference: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 87, 19-27 (2014)
Subject: Photogrammetry and Remote Sensing.
Keywords: Change detection; Object-based; Pixel-based; Misregistration; High-spatial resolution; Accuracy assessment.
Abstract: High-spatial resolution remote sensing imagery provides unique opportunities for detailed characterization and monitoring of landscape dynamics. To better handle such data sets, change detection using the object-based paradigm, i.e., object-based change detection (OBCD), have demonstrated improved performances over the classic pixel-based paradigm. However, image registration remains a critical pre-process, with new challenges arising, because objects in OBCD are of various sizes and shapes. In this study, we quantified the effects of misregistration on OBCD using high-spatial resolution SPOT 5 imagery (5 m) for three types of landscapes dominated by urban, suburban and rural features, representing diverse geographic objects. The experiments were conducted in four steps: (i) Images were purposely shifted to simulate the misregistration effect. (ii) Image differencing change detection was employed to generate difference images with all the image-objects projected to a feature space consisting of both spectral and texture variables. (iii) The changes were extracted using the Mahalanobis distance and a change ratio. (iv) The results were compared to the ' real ' changes from the image pairs that contained no purposely introduced registration error. A pixel-based change detection method using similar steps was also developed for comparisons. Results indicate that misregistration had a relatively low impact on object size and shape for most areas. When the landscape is comprised of small mean object sizes (e.g., in urban and suburban areas), the mean size of ' change ' objects was smaller than the mean of all objects and their size discrepancy became larger with the decrease in object size. Compared to the results using the pixel-based paradigm, OBCD was less sensitive to the misregistration effect, and the sensitivity further decreased with an increase in local mean object size. However, high-spatial resolution images typically have higher spectral variability within neighboring pixels than the relatively low resolution datasets. As a result, accurate image registration remains crucial to change detection even if an object-based approach is used.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
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