bluuebunny/crossref_metadata_2025 huggingface dataset:
GAP FILLING IN ROAD EXTRACTION USING RADON TRANSFORMATION
Authors: Matkan A. A. , Hajeb M. , Eslami M. , Pourali H. , Sadeghian S.
Published: October 2012

Abstract. Road information has a key role in many applications such as transportation, automatic navigation, traffic management, crisis management, and also to facilitate and accelerate updating databases in a GIS. Therefore in the past two decades, automatic road extraction has become an important issue in remote sensing, photogrammetry and computer vision. An essential challenge in road extraction process is filling the gaps which have appeared due to getting placed under trees, tunnels or any other reason. Connection of roads is a momentous topological property that is necessity to perform most of the spatial analyses. Hence, Gap filling is an important post-process. The main aim of this paper is to provide a method which is applicable in road extraction algorithms to automatic fill the gaps. The proposed algorithm is based on Radon transformation and has four stags. In the first stage, detected road are thinned insofar as one pixel width is achieved. Then endpoints are detected. In the second stage, regarding to some constraints those endpoints which do not belong to any gaps are identified and deleted from endpoints list. In the third stage, the real gaps are found using the road direction computed by used of Radon technique. In fourth stage, the selected endpoints are connected together using Spline interpolation. This algorithm is applied on several datasets and also on a real detected road. The experimental results show that the proposed algorithm has good performance on straight roads but it does not work well in intersections, due to being direction-oriented.
DENSE CORRESPONDING PIXEL MATCHING USING A FIXED WINDOW WITH RGB INDEPENDENT INFORMATION
Authors: Hu T. , Wu H.
Published: October 2012

Abstract. A fixed window based algorithm is proposed for dense corresponding pixel matching between epipolar images. The algorithm makes full use of three colour components, R, G and B, and simplifies determining the optimal window. In order to reduce the impact of obvious colour difference between corresponding pixels, the proposed algorithm uses more information from neighbouring pixels, and adjusts the contributions of the central pixels and their neighbouring pixels. Two pairs of epipolar images are tested using the fixed window based algorithm. The experimental results, including boundary testing, sampled corresponding pixels’ evaluation and 3D point cloud check, demonstrate the effectiveness of the proposed algorithm.
COMPARATIVE PERFORMANCE ANALYSIS OF A HYPER-TEMPORAL NDVI ANALYSIS APPROACH AND A LANDSCAPE-ECOLOGICAL MAPPING APPROACH
Authors: Ali A. , de Bie C. A. J. M , Scarrott R. G. , Ha N. T. T. , Skidmore A. K.
Published: October 2012

Abstract. Both agricultural area expansion and intensification are necessary to cope with the growing demand for food, and the growing threat of food insecurity which is rapidly engulfing poor and under-privileged sections of the global population. Therefore, it is of paramount importance to have the ability to accurately estimate crop area and spatial distribution. Remote sensing has become a valuable tool for estimating and mapping cropland areas, useful in food security monitoring. This work contributes to addressing this broad issue, focusing on the comparative performance analysis of two mapping approaches (i) a hyper-temporal Normalized Difference Vegetation Index (NDVI) analysis approach and (ii) a Landscape-ecological approach. The hyper-temporal NDVI analysis approach utilized SPOT 10-day NDVI imagery from April 1998–December 2008, whilst the Landscape-ecological approach used multitemporal Landsat-7 ETM+ imagery acquired intermittently between 1992 and 2002. Pixels in the time-series NDVI dataset were clustered using an ISODATA clustering algorithm adapted to determine the optimal number of pixel clusters to successfully generalize hyper-temporal datasets. Clusters were then characterized with crop cycle information, and flooding information to produce an NDVI unit map of rice classes with flood regime and NDVI profile information. A Landscape-ecological map was generated using a combination of digitized homogenous map units in the Landsat-7 ETM+ imagery, a Land use map 2005 of the Mekong delta, and supplementary datasets on the regions terrain, geo-morphology and flooding depths. The output maps were validated using reported crop statistics, and regression analyses were used to ascertain the relationship between land use area estimated from maps, and those reported in district crop statistics. The regression analysis showed that the hyper-temporal NDVI analysis approach explained 74% and 76% of the variability in reported crop statistics in two rice crop and three rice crop land use systems respectively. In contrast, 64% and 63% of the variability was explained respectively by the Landscape-ecological map. Overall, the results indicate the hyper-temporal NDVI analysis approach is more accurate and more useful in exploring when, why and how agricultural land use manifests itself in space and time. Furthermore, the NDVI analysis approach was found to be easier to implement, was more cost effective, and involved less subjective user intervention than the landscape-ecological approach.
CONTEXT MODELS FOR CRF-BASED CLASSIFICATION OF MULTITEMPORAL REMOTE SENSING DATA
Authors: Hoberg T. , Rottensteiner F. , Heipke C.
Published: October 2012

Abstract. The increasing availability of multitemporal satellite remote sensing data offers new potential for land cover analysis. By combining data acquired at different epochs it is possible both to improve the classification accuracy and to analyse land cover changes at a high frequency. A simultaneous classification of images from different epochs that is also capable of detecting changes is achieved by a new classification technique based on Conditional Random Fields (CRF). CRF provide a probabilistic classification framework including local spatial and temporal context. Although context is known to improve image analysis results, so far only little research was carried out on how to model it. Taking into account context is the main benefit of CRF in comparison to many other classification methods. Context can be already considered by the choice of features and in the design of the interaction potentials that model the dependencies of interacting sites in the CRF. In this paper, these aspects are more thoroughly investigated. The impact of the applied features on the classification result as well as different models for the spatial interaction potentials are evaluated and compared to the purely label-based Markov Random Field model.
CHARACTERISING VEGETATED SURFACES USING MODIS MULTIANGULAR SATELLITE DATA
Authors: McCamley G. , Grant I. , Jones S. , Bellman C.
Published: October 2012

Abstract. Bidirectional Reflectance Distribution Functions (BRDF) seek to represent variations in surface reflectance resulting from changes in a satellite's view and solar illumination angles. BRDF representations have been widely used to assist in the characterisation of vegetation. However BRDF effects are often noisy, difficult to interpret and are the spatial integral of all the individual surface features present in a pixel. This paper describes the results of an approach to understanding how BRDF effects can be used to characterise vegetation. The implementation of the Ross Thick Li Sparse BRDF model using MODIS is a stable, mature data product with a 10 year history and is a ready data source. Using this dataset, a geometric optical model is proposed that seeks to interpret the BRDF effects in terms of Normalised Difference Vegetation Index (NDVI) and a height-to-width ratio of the vegetation components. The height-to-width ratio derived from this model seeks to represent the dependence of NDVI to changes in view zenith angle as a single numeric value. The model proposed within this paper has been applied to MODIS pixels in central Australia for areas in excess of 18,000 km2. The study area is predominantly arid and sparsely vegetated which provides a level of temporal and spatial homogeneity. The selected study area also minimises the effects associated with mutual obscuration of vegetation which is not considered by the model. The results are represented as a map and compared to NDVI derived from MODIS and NDVI derived from Landsat mosaics developed for Australia's National Carbon Accounting System (NCAS). The model reveals additional information not obvious in reflectance data. For example, the height-to-width ratio is able to reveal vegetation features in arid areas that do not have an accompanying significant increase in NDVI derived from MODIS, i.e. the height-to-width ratio reveals vegetation which is otherwise only apparent in NDVI derived from the higher resolution Landsat NCAS dataset.
A NOVEL APPROACH TO SUPER RESOLUTION MAPPING OF MULTISPECTRAL IMAGERY BASED ON PIXEL SWAPPING TECHNIQUE
Authors: Niroumand Jadidi M. , Safdarinezhad A. R. , Sahebi M. R. , Mokhtarzade M.
Published: October 2012

Abstract. Mixed pixels could be considered as a major source of uncertainty through classification process of satellite imagery. In this regard, the use of soft classifiers is often inevitable in order to increase the accuracy of land cover estimates. Although soft classifiers provide detailed information for each pixel, spatial arrangement of sub-pixels remains unknown. Super Resolution Mapping (SRM) has opened up a new horizon to produce finer spatial resolution maps using the outputs of soft classifiers. Wide variety of SRM algorithms has been developed. In this way, spatial optimisation techniques are the most applicable ones. However, random allocation of sub-pixels and iterative procedure of optimisation are the main limitations of current methods (e.g. Hopfield Neural Network, Simulated Annealing). This research attempts to provide an optimisation approach based on the pixel swapping technique in order to simplify the concept and to reduce the iteration procedure. In this paper, a brief survey is conducted on spatial optimisation based techniques of SRM. SVM and SMACC are used cooperatively to produce fractional maps as an input of SRM algorithm. The initial allocation of the sub-pixels is performed non-randomised based on the highest amounts of attractiveness. An optimisation procedure is proposed to transfer the multiple allocated sub-pixels to the non-allocated ones. This procedure usually stops with minimal iterations and is time effective. The proposed method is tested on multispectral imagery (Landsat ETM+ and Quickbird) and has demonstrated precise results particularly in boundary pixels.
IMPROVING MARKOV RANDOM FIELD BASED SUPER RESOLUTION MAPPING THROUGH FUZZY PARAMETER INTEGRATION
Authors: Welikanna D. R . , Tamura M. , Tolpekin V. A. , Susaki J. , Maki M.
Published: October 2012

Abstract. The objective of this study was to improve the Markov Random Field (MRF) based Super Resolution Mapping (SRM) technique to account for the vague land-cover interpretations (class mixture and the intermediate conditions) in an urban area. The algorithm has been improved to integrate the fuzzy mean and fuzzy covariance measurements, to a MRF based SRM scheme to optimize the classification results. The technique was tested on a WORLDVIEW-2 data set, acquired over a highway construction area, in Colombo, Sri Lanka. Based on the visual interpretation of the image, three major land-cover types of this area were identified for the study; those were vegetation, soil and exposed grass and impervious surface with low medium and high albedo. The membership values for each pixel were determined from training samples through Spectral Angle Mapper (SAM) technique. The compulsory fuzzy mean and the covariance measurements were derived using these membership grades, and subsequently was applied in MRF based SRM technique. The primary reference data was generated using Maximum Likelihood Classification (MLC) performed on the same data which was resampled to 1m resolution. The scale factor was set to be (S) = 2, to generate SRM of 1m resolution. The smoothening parameter (λ) which balances the prior and likelihood energy terms were tested in the range from 0.3 to 0.9. SRM were generated using fuzzy MRF and the conventional MRF models respectively. Results suggest that the fuzzy integrated model has improved the results with an overall accuracy of 85.60% and kappa value of 0.78 between the optimal results and the reference data, while in the conventional case it was 77.81% of overall accuracy with kappa being 0.65. Among the two MRF models, fuzzy parameter integrated model shows the highest agreement with class fractions from the reference image with a smallest average _MAE (MAE, Mean Absolute Error) of 0.03.
DETECTION AND QUANTIFICATION OF MORPHOLOGICAL CHANGES USING MULTI-RESOLUTION TERRESTRIAL LASER SCANS
Authors: Arav R. , Filin S.
Published: October 2012

Abstract. Detection of changes is a key component for monitoring the dynamics of natural environments. Such detection is involved because of the complexity of such environments and the level of detail that is required in some cases. Answering the need for a detailed documentation of such changes in the surface morphology, a growing number of studies are making use of laser scanners, airborne and terrestrial, to detect and quantify them. For the change detection itself, many point-cloud processing based approaches interpolate the data and apply a pixel-based subtraction. Such analyses are 2.5D by nature and assume a change along fixed trend (mostly vertical) which does not reflect actual surface modifications in complex topographical settings. We propose in this paper a novel methodology for detection of changes in such environments. Our model involves no data loss, is three-dimensional, and requires no imposition of external constraints (e.g., enforcement of a positional constraint). Unique features of the proposed methodology lie in its ability to process point clouds with an unfixed scan resolution, and multiple scans from the same epoch. The proposed model is demonstrated on a site along the Mediterranean coastal cliff, a complex environment which undergoes rapid changes. The proposed model enables the detection of centimeter-scale changes as well as meter-size changes. These results serve as a basis for linking measured deformations with cliff-scale collapse mechanisms in order to gain a more comprehensive understanding of the cliff erosional processes and possibly identify collapse precursors such as crack widening and mass creep.
LAND COVER CHANGE KNOWLEDGE REPRESENTATION USING TEMPORAL LOGIC AND OPERATION RELATIONS
Authors: Chen J. , Wu H.
Published: October 2012

Abstract. The knowledge representation model plays an important role in the dynamic extracting and web service for Land Cover Change. While former researches were mainly focus on the represent of Spatio-temporal data itself, fewer of them attended the relations of Land Cover Information associated with change. This paper focused on the Land Cover Representation Model, and presented a knowledge representation model for LCC using temporal logic and operation relations. Firstly, we analyses the new demanding of change information service for web publishing, secondly, we discuss the temporal logic of snapshots and increments; then we introduce a method to represent the operation relations between snapshots and increments; finally we construct a knowledge representation for Land Cover Change based on the temporal logic and operation relations.
SUPERPIXEL-BASED UNSUPERVISED CHANGE DETECTION USING MULTI-DIMENSIONAL CHANGE VECTOR ANALYSIS AND SVM-BASED CLASSIFICATION
Authors: Wu Z. , Hu Z. , Fan Q.
Published: October 2012

Abstract. In this paper, a novel superpixel-based approach is introduced for unsupervised change detection using remote sensing images. The proposed approach contains three steps: 1) Superpixel segmentation. The simple linear iterative cluster (SLIC) algorithm is applied to obtain lattice-like homogenous superpixels. To avoid discordances of the superpixel boundaries obtained from bi-temporal images, the two images are firstly fused using principle component analysis. And then, the SLIC algorithm is applied on the first three principle components, which contain the main information of the two images. 2) For each superpixel, which is considered as the basic unit of the image space, the multi-dimensional change vector is computed from spectral, textural and structural features. 3) The superpixels are classified into two type: changed and unchanged through two progressive classification processes. The superpixels are firstly cataloged into three types: changed, unchanged and undefined by thresholding the change vectors and a voting process. And then the undefined superpixels are further classified into two classes: changed and unchanged, using a SVM-based classifier, which is trained by the derived changed and unchanged superpixels from the former step. The experiment using Indonesia data set has confirmed that the proposed approach is able to detect the changes automatically, by exploiting multiple change features.
A SHAPE SIMILARITY BASED CHANGE DETECTION APPROACH OF MULTI-RESOLUTION REMOTE SENSING IMAGES
Authors: Zhang P. , Lv Z. , Zhang D. , Chen J.
Published: October 2012

Abstract. While a lot of approaches have been reported for change detection using high resolution images, few approaches which are used for different scale images have been developed. This paper proposes a change detection approach which can be used to detect the change in images with different scales. In this study, image objects have been extracted from images obtained at different time. Then, descriptors to describe the shape features are constructed. After that, the similarity between corresponding features is defined to measure whether the observed objects changed or not. To test the proposed approach, a test for building change detection is designed. The accuracy of our result is 96.1%, which is distinctly higher than that of conventional method 81.9%. The primary results show that the proposed approach is feasible and valid.
HIGH RESOLUTION DEFORMATION TIME SERIES ESTIMATION FOR DISTRIBUTED SCATTERERS USING TERRASAR-X DATA
Authors: Goel K. , Adam N.
Published: October 2012

Abstract. In recent years, several SAR satellites such as TerraSAR-X, COSMO-SkyMed and Radarsat-2 have been launched. These satellites provide high resolution data suitable for sophisticated interferometric applications. With shorter repeat cycles, smaller orbital tubes and higher bandwidth of the satellites; deformation time series analysis of distributed scatterers (DSs) is now supported by a practical data basis. Techniques for exploiting DSs in non-urban (rural) areas include the Small Baseline Subset Algorithm (SBAS). However, it involves spatial phase unwrapping, and phase unwrapping errors are typically encountered in rural areas and are difficult to detect. In addition, the SBAS technique involves a rectangular multilooking of the differential interferograms to reduce phase noise, resulting in a loss of resolution and superposition of different objects on ground. In this paper, we introduce a new approach for deformation monitoring with a focus on DSs, wherein, there is no need to unwrap the differential interferograms and the deformation is mapped at object resolution. It is based on a robust object adaptive parameter estimation using single look differential interferograms, where, the local tilts of deformation velocity and local slopes of residual DEM in range and azimuth directions are estimated. We present here the technical details and a processing example of this newly developed algorithm.
RADIOMETRIC CALIBRATION OF MULTI-WAVELENGTH AIRBORNE LASER SCANNING DATA
Authors: Briese C. , Pfennigbauer M. , Lehner H. , Ullrich A. , Wagner W. , Pfeifer N.
Published: October 2012

Abstract. Airborne laser scanning (ALS) is a widely used technique for the sampling of the earth's surface. Nowadays a wide range of ALS sensor systems with different technical specifications can be found. One parameter is the laser wavelength which leads to a sensitivity for the wavelength dependent backscatter characteristic of sensed surfaces. Current ALS sensors usually record next to the geometric information additional information on the recorded signal strength of each echo. In order to utilize this information for the study of the backscatter characteristic of the sensed surface, radiometric calibration is essential. This paper focuses on the radiometric calibration of multi-wavelength ALS data and is based on previous work on the topic of radiometric calibration of monochromatic (single-wavelength) ALS data. After a short introduction the theory and whole workflow for calibrating ALS data radiometrically based on in-situ reference surfaces is presented. Furthermore, it is demonstrated that this approach for the monochromatic calibration can be used for each channel of multi-wavelength ALS data. The resulting active multi-channel radiometric image does not have any shadows and from a geometric viewpoint the position of the objects on top of the terrain surface is not altered (the result is a multi-channel true orthophoto). Within this paper the approach is demonstrated by three different single-wavelength ALS data acquisition campaigns (532nm, 1064nm and 1550nm) covering the area of the city Horn (Austria). The results and practical issues are discussed.
IDENTIFYING STANDING DEAD TREES IN FOREST AREAS BASED ON 3D SINGLE TREE DETECTION FROM FULL WAVEFORM LIDAR DATA
Authors: Yao W. , Krzystek P. , Heurich M.
Published: October 2012

Abstract. In forest ecology, a snag refers to a standing, partly or completely dead tree, often missing a top or most of the smaller branches. The accurate estimation of live and dead biomass in forested ecosystems is important for studies of carbon dynamics, biodiversity, and forest management. Therefore, an understanding of its availability and spatial distribution is required. So far, LiDAR remote sensing has been successfully used to assess live trees and their biomass, but studies focusing on dead trees are rare. The paper develops a methodology for retrieving individual dead trees in a mixed mountain forest using features that are derived from small-footprint airborne full waveform LIDAR data. First, 3D coordinates of the laser beam reflections, the pulse intensity and width are extracted by waveform decomposition. Secondly, 3D single trees are detected by an integrated approach, which delineates both dominate tree crowns and understory small trees in the canopy height model (CHM) using the watershed algorithm followed by applying normalized cuts segmentation to merged watershed areas. Thus, single trees can be obtained as 3D point segments associated with waveform-specific features per point. Furthermore, the tree segments are delivered to feature definition process to derive geometric and reflectional features at single tree level, e.g. volume and maximal diameter of crown, mean intensity, gap fraction, etc. Finally, the spanned feature space for the tree segments is forwarded to a binary classifier using support vector machine (SVM) in order to discriminate dead trees from the living ones. The methodology is applied to datasets that have been captured with the Riegl LMSQ560 laser scanner at a point density of 25 points/m2 in the Bavarian Forest National Park, Germany, respectively under leaf-on and leaf-off conditions for Norway spruces, European beeches and Sycamore maples. The classification experiments lead in the best case to an overall accuracy of 73% in a leaf-on situation and 71% in a leaf-off situation, if we assess the classification results using 5-fold cross-validation method with the help of reference data acquired by the field surveying.
SPECTRAL INFORMATION RETRIEVAL FOR SUB-PIXEL BUILDING EDGE DETECTION
Authors: Avbelj J.
Published: October 2012

Abstract. Building extraction from imagery has been an active research area for decades. However, the precise building detection from hyperspectral (HSI) images solely is a less often addressed research question due to the low spatial resolution of data. The building boundaries are usually represented by spectrally mixed pixels, and classical edge detector algorithms fail to detect borders with sufficient completeness. The idea of the proposed method is to use fraction of materials in mixed pixels to derive weights for adjusting building boundaries. The building regions are detected using seeded region growing and merging in a HSI image; for the initial seed point selection the digital surface model (DSM) is used. Prior to region growing, the seeds are statistically tested for outliers on the basis of their spectral characteristics. Then, the border pixels of building regions are compared in spectrum to the seed points by calculating spectral dissimilarity. From this spectral dissimilarity the weights for weighted and constrained least squares (LS) adjustment are derived. We used the Spectral Angle Mapper (SAM) for spectral similarity measure, but the proposed boundary estimation method could benefit from soft classification or spectral unmixing results. The method was tested on a HSI image with spatial resolution of 4 m, and buildings of rectangular shape. The importance of constraints to the relations between building parts, e.g. perpendicularity is shown on example with a building with inner yards. The adjusted building boundaries are compared to the laser DSM, and have a relative accuracy of boundaries 1/4 of a pixel.
A CLASSIFICATION ALGORITHM FOR HYPERSPECTRAL DATA BASED ON SYNERGETICS THEORY
Authors: Cerra D. , Mueller R. , Reinartz P.
Published: October 2012

Abstract. This paper presents a new classification methodology for hyperspectral data based on synergetics theory, which describes the spontaneous formation of patterns and structures in a system through self-organization. We introduce a representation for hyperspectral data, in which a spectrum can be projected in a space spanned by a set of user-defined prototype vectors, which belong to some classes of interest. Each test vector is attracted by a final state associated to a prototype, and can be thus classified. As typical synergetics-based systems have the drawback of a rigid training step, we modify it to allow the selection of user-defined training areas, used to weight the prototype vectors through attention parameters and to produce a more accurate classification map through majority voting of independent classifications. Results are comparable to state of the art classification methodologies, both general and specific to hyperspectral data and, as each classification is based on a single training sample per class, the proposed technique would be particularly effective in tasks where only a small training dataset is available.
SUPER-RESOLUTION OF HYPERSPECTRAL IMAGES USING COMPRESSIVE SENSING BASED APPROACH
Authors: Patel R. C. , Joshi M. V.
Published: October 2012

Abstract. Over the past decade hyper spectral (HS) image analysis has turned into one of the most powerful and growing technologies in the field of remote sensing. While HS images cover large area at fine spectral resolution, their spatial resolutions are often too coarse for the use in various applications. Hence improving their resolution has a high payoff. This paper presents a novel approach for super-resolution (SR) of HS images using compressive sensing (CS). Besides ill-posedness of SR problem, the main challenge in HS super-resolution is to preserve spectral contents among all bands while increasing their spatial resolutions. In this work, we first obtain an initial estimate of the super-resolution on a reduced dimension HS data. The HS observations of different wavelengths are represented as linear combination of smaller number of basis image planes (BIPs) using principal component analysis (PCA). The novelty of our approach lies in using CS based approach to super-resolve the most informative PCA transformed image representing highest spectral variance (i.e. the first principal component). Our approach uses low and high spatial resolution dictionaries of patches generated by random sampling of raw patches of PCA transformed images that are generated using the training images having similar statistical properties. Using the sparsity constraint, low resolution test patch is represented as a sparse linear combination of relevant dictionary elements adaptively, that leads to initial estimate of super-resolved PCA image having maximum spectral variability. Since SR is an ill-posed problem, we obtain the final solution using a regularization framework considering the sparse coefficients obtained by the CS approach and the autoregressive (AR) parameters obtained from the initial estimate. The remaining PCA images are up-scaled using regularization, considering the same AR parameters which were obtained from super-resolved PCA image having maximum spectral variability. Experiments are conducted on real HS images collected by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Visual inspections and quantitative comparison confirm that our method enhances spatial information without introducing significant spectral distortion.
OPTIMIZING OBJECT-BASED CLASSIFICATION IN URBAN ENVIRONMENTS USING VERY HIGH RESOLUTION GEOEYE-1 IMAGERY
Authors: Aguilar M. A. , Vicente R. , Aguilar F. J. , Fernández A. , Saldaña M. M.
Published: October 2012

Abstract. The latest breed of very high resolution (VHR) commercial satellites opens new possibilities for cartographic and remote sensing applications. In fact, one of the most common applications of remote sensing images is the extraction of land cover information for digital image base maps by means of classification techniques. When VHR satellite images are used, an object-based classification strategy can potentially improve classification accuracy compared to pixel based classification. The aim of this work is to carry out an accuracy assessment test on the classification accuracy in urban environments using pansharpened and panchromatic GeoEye-1 orthoimages. In this work, the influence on object-based supervised classification accuracy is evaluated with regard to the sets of image object (IO) features used for classification of the land cover classes selected. For the classification phase the nearest neighbour classifier and the eCognition v. 8 software were used, using seven sets of IO features, including texture, geometry and the principal layer values features. The IOs were attained by eCognition using a multiresolution segmentation approach that is a bottom-up regionmerging technique starting with one-pixel. Four different sets or repetitions of training samples, always representing a 10% for each classes were extracted from IOs while the remaining objects were used for accuracy validation. A statistical test was carried out in order to strengthen the conclusions. An overall accuracy of 79.4% was attained with the panchromatic, red, blue, green and near infrared (NIR) bands from the panchromatic and pansharpened orthoimages, the brightness computed for the red, blue, green and infrared bands, the Maximum Difference, a mean of soil-adjusted vegetation index (SAVI), and, finally the normalized Digital Surface Model or Object Model (nDSM), computed from LiDAR data. For buildings classification, nDSM was the most important feature attaining producer and user accuracies of around 95%. On the other hand, for the class "vegetation", SAVI was the most significant feature, obtaining accuracies close to 90%.
Transition in the fractal geometry of Arctic melt ponds
Authors: Hohenegger C. , Alali B. , Steffen K. R. , Perovich D. K. , Golden K. M.
Published: October 2012

Abstract. During the Arctic melt season, the sea ice surface undergoes a remarkable transformation from vast expanses of snow covered ice to complex mosaics of ice and melt ponds. Sea ice albedo, a key parameter in climate modeling, is determined by the complex evolution of melt pond configurations. In fact, ice–albedo feedback has played a major role in the recent declines of the summer Arctic sea ice pack. However, understanding melt pond evolution remains a significant challenge to improving climate projections. By analyzing area–perimeter data from hundreds of thousands of melt ponds, we find here an unexpected separation of scales, where pond fractal dimension D transitions from 1 to 2 around a critical length scale of 100 m2 in area. Pond complexity increases rapidly through the transition as smaller ponds coalesce to form large connected regions, and reaches a maximum for ponds larger than 1000 m2, whose boundaries resemble space-filling curves, with D ≈ 2. These universal features of Arctic melt pond evolution are similar to phase transitions in statistical physics. The results impact sea ice albedo, the transmitted radiation fields under melting sea ice, the heat balance of sea ice and the upper ocean, and biological productivity such as under ice phytoplankton blooms.
Surface undulations of Antarctic ice streams tightly controlled by bedrock topography
Authors: De Rydt J. , Gudmundsson G. H. , Corr H. F. J. , Christoffersen P.
Published: October 2012

Abstract. Full Stokes models predict that fast-flowing ice streams transmit information about their bedrock topography most efficiently to the surface for basal undulations with length scales between 1 and 20 times the mean ice thickness. This typical behaviour is independent on the precise values of the flow law and sliding law exponents, and should be universally observable. However, no experimental evidence for this important theoretical prediction has been obtained so far, hence ignoring an important test for the physical validity of current-day ice flow models. In our work we use recently acquired airborne radar data for the Rutford Ice Stream and Evans Ice Stream, and we show that the surface response of fast-flowing ice is highly sensitive to bedrock irregularities with wavelengths of several ice thicknesses. The sensitivity depends on the slip ratio, i.e. the ratio between mean basal sliding velocity and mean deformational velocity. We find that higher values of the slip ratio generally lead to a more efficient transfer, whereas the transfer is significantly dampened for ice that attains most of its surface velocity by creep. Our findings underline the importance of bedrock topography for ice stream dynamics on spatial scales up to 20 times the mean ice thickness. Our results also suggest that local variations in the flow regime and surface topography at this spatial scale cannot be explained by variations in basal slipperiness.
Influence of Surgical Approach on Postoperative Femoral Bone Remodelling after Cementless Total Hip Arthroplasty
Authors: Merle Christian , Sommer Julia , Streit Marcus R. , Waldstein Wenzel , Bruckner Thomas , Parsch Dominik , Aldinger Peter R. , Gotterbarm Tobias
Published: October 2012

We retrospectively evaluated the femoral periprosthetic bone mineral density (BMD) in a consecutive series of patients who had undergone total hip arthroplasty (THA) with a straight, double tapered cementless stem using a muscle-sparing anterolateral (group A) and the transgluteal (group B) surgical approach. Dual-energy x-ray absorptiometry (DXA) measurements were performed in the first postoperative week (t1), and after 3 (t2), 6 (t3) and 12 months (t4) using an identical protocol. Patients were clinically and radiographically evaluated at final follow-up (t4).

A complete set of four consecutive DXA measurements was obtained for 16 hips in group A and 26 hips in group B. In patients in whom the transgluteal approach was used (Group B), we observed a significantly greater decline in overall periprosthetic BMD (netavg) and in BMD in the periprosthetic regions of interest (ROI) 1, 4, 5 and 6 between t1 and t4. At clinical and radiographic evaluation at t4, no differences between the groups were detected.

Femoral periprosthetic BMD is affected by the selected surgical approach in the first postoperative year. This might be attributed to altered femoral loading as a result of differences in intraoperative damage to the abductor muscles.
Two-Year Radiologic Assessment of the Trident Peripheral Self-Locking Cup Using Ebra
Authors: Nunag Perrico , Deakin Angela H. , Oburu Ezekiel , Sarungi Martin
Published: October 2012

We performed a radiologic assessment of the Trident Peripheral Self-Locking cup 2 years after implantation to assess early migration behaviour and to establish if incomplete postoperative seating correlated with early instability.

A retrospective analysis of 30 cases was performed using EBRA. No cups had acetabular screws. Average total migration was 1.5 mm (range 0.1 to 5.9 mm). Seventeen showed total migration >1 mm and 7 of these showed further migration >2 mm (range 2.3 to 5.9 mm). Twenty cups demonstrated incomplete seating on initial post-operative radiographs (mean 1.4 mm, range 0.3 to 3.0). No relationship between incomplete seating and migration was identified (p = 0.86). The majority of gaps consolidated at differing times within the 2 year period. Oxford Hip scores showed significant improvement after surgery (p = 0.001) and this was independent of migration (p = 0.76). At 5 years there were no revisions for aseptic loosening.

Conclusion

The majority of the cups demonstrated early radiographic instability, and this was not related to incomplete seating. Five year functional outcome appears good and independent of migration and initial seating.
Molecular Identification and Ecology of a Newly Discovered Population of Sun Catfish Horabagrus brachysoma from Northern Western Ghats of India
Authors: Katwate Unmesh , Raut Rupesh , Khot Mayura , Paingankar Mandar , Dahanukar Neelesh
Published: October 2012

Horabagrus brachysoma, thought to be endemic to the southern parts of the Western Ghats of India, is recorded for the northern parts of the Western Ghats, extending the species distribution range by 180 km. We have confirmed the identity of the species and the fact that the species is indigenous to this area and not an artifact of recent introductions using molecular methods. Apart from the range extension we have also provided detailed analysis regarding the nature of morphometric variations between the sexes, length-weight relationship, and a brief discussion about the potential habitat requirements and threats to this species. By documenting the possible threats to this threatened and endemic species, we have commented on the possible measures to conserve the species in the wild.
Nutritional Intervention Incorporating Expedited 10 g Protein Counter (EP-10) to Improve the Albumin and Transferrin of Chronic Hemodialysis Patients
Authors: Lim Su-Lin , Lye Jamie
Published: October 2012

Objective. The expedited 10 g protein counter (EP-10) is a quick and valid clinical tool for dietary protein quantification. This study aims to assess the clinical effectiveness of the EP-10 in improving serum albumin and transferrin in chronic hemodialysis patients.Methods. Forty-five patients with low serum albumin (<38 g/L) were enrolled in this study. Parameters measured included dry weight, height, dietary intake, and levels of serum albumin, transferrin, potassium, phosphate, and kinetic modeling(Kt/V).The nutritional intervention incorporated the EP-10 in two ways (1) to quantify protein intake of patients and (2) to educate patients to meet their protein requirements. Mean values of the nutritional parameters before and after intervention were compared using pairedt-test.Results. Three months after nutritional intervention, mean albumin levels increased significantly from 32.2 ± 4.8 g/L to 37.0 ± 3.2 g/L(P<0.001). Thirty-eight (84%) patients showed an increase in albumin levels, while two (4%) maintained their levels. Of the thirty-six (80%) patients with low transferrin levels (<200 mg/dL), 28 (78%) had an increase and two maintained their levels after intervention. Mean transferrin levels increased significantly from 169.4 ± 39.9 mg/dL to 180.9 ± 38.1 mg/dL(P<0.05).Conclusion. Nutritional intervention incorporating the EP-10 method is able to make significant improvements to albumin and transferrin levels of chronic hemodialysis patients.
The Use of Response Surface Methodology as a Statistical Tool for Media Optimization in Lipase Production from the Dairy Effluent Isolate Fusarium solani
Authors: Kanmani P. , Karthik S. , Aravind J. , Kumaresan K.
Published: October 2012

The optimization of extracellular lipase production by Fusarium isolani strain SKWF7 isolated from dairy wastewater was carried out in this study. Initially, the physicochemical factors significantly influencing enzyme production were studied by varying one-factor-at-a-time (OFAT). A mesophilic temperature of 40°C, alkaline pH of 8, and incubation period of 72 hours were found to be the optimal conditions for lipase production. Among the media components, the disaccharide sucrose acted as the best carbon source; palm oil as the best inducing lipid substrate; casein and (NH4)2SO4 as the best organic and inorganic nitrogen sources; Ca2+ ion as the best trace element. In the next phase of work, statistical optimization of medium components was performed by employing the Box-Behnken design of Response Surface Methodology (RSM). The optimum concentrations of three significant factors, namely, palm oil, (NH4)2SO4, and CaCO3 were determined by this method to be 5% (v/v), 5.5 g/L, and 0.1 g/L, respectively. RSM-guided design of experiments resulted in a maximum lipase production of 73.3 U/ml, which is a 1.7-fold increase in comparison with that obtained in the unoptimized medium. These results point towards the success of the model in developing a process for the production of lipase, an enzyme of enormous industrial significance.
Ceremonies of the Pawnee, Part I: The Skiri
Authors: Murie James R.
Published: October 2012

In 1910 James R. Murie, an educated Pawnee who had previously worked with various anthropologists (most notably Alice C. Fletcher and George A. Dorsey), was given a grant by the Bureau of American Ethnology to prepare a full account of surviving Pawnee religious ceremonies. Shortly afterward he began work under the direction of Clark Wissler, with whom he planned a comprehensive description of the ritualism of the Skiri band. This monograph, written by Murie in collaboration with Clark Wissler, is the combined result of the two projects, which extended over a decade. It is a detailed presentation of the essential features of Pawnee ceremonialism. The first part presents the annual cycle of Skiri ceremonial life, minutely describing most of the rituals as well as the role and functions of sacred bundles in the culture. The second part includes accounts of three surviving South Band ceremonies that Murie witnessed: the White Beaver Ceremony (or Doctor Dance) of the Chawi band, and the Bear and Buffalo dances of the Pitahawirata band. In each of the accounts the songs of the ceremony are given in both Pawnee and English. Together they constitute one of the most extensive song collections for any North American tribe. For the three South Band ceremonies, the vision stories underlying the songs are also presented. The manuscript, scheduled for publication on several occasions in the 1920s and 1930s, has been in the archives of the Bureau of American Ethnology since 1921. It is here presented in edited form, together with revised linguistic transcriptions and translations, notes, an expanded bibliography, a biography of Murie, and two indexes.
The Great Tzotzil Dictionary of Santo Domingo Zinacantan with Grammatical Analysis and Historical Commentary, Volume I: Tzotzil-English
Authors: Laughlin Robert M. , Haviland John B.
Published: October 2012

This dictionary of Tzotzil (Mayan) vocabulary from the town of Zinacantan, Chiapas, Mexico, was edited by the author over a period of nine years. The original manuscript, compiled by an anonymous Domimcan friar, probably at the close of the 16th century, disappeared during the Mexican Revolution but a manuscript copy of 351 pages survives. It was made around 1906 at the behest of the Bishop of Chiapas, Francisco Orozco y Jimenez. The approximately 11,000 Spanish-Tzotzil entnes have been translated into English. Following the format of The Great Tzotzil Dictionary of San Lorenzo Zinacantan. the colonial Tzotzil has been ordered by roots. The spelling has been corrected and modernized. Doubtful interpretations are stated and problems are brought to the reader's attention, with frequent reference to the existing colonial Tzeltal dictionaries. Ea.ch entry is analyzed grammatically according to a system devised by John B. Haviland. All entries are keyed to their original location in the manuscript copy. A second section provides an English-Tzotzil dictionary and index for the thesaurus that follows. To make the cultural contents of this dictionary more readily available to anthropologists and historians, the thesaurus groups the Tzotzil terms under 36 cultural categories such as world, movement, life cycle, emotions, agriculture, ailments, religion, etc. Of special interest is metaphoric speech, subdivided into 10 categories. A third section presents the Spanish-Tzotzil dictionary slightly abbreviated and with the spelling of both languages modernized. A facsimile of the manuscript copy is also offered. Preceding the dictionaries is a historical sketch that places the original in its colonial setting, compares it to other 16th and 17th century lexicographic efforts, and suggests a possible author. The lives of the five individuals responsible for the preservation of the manuscript copy are traced. John B. Haviland, drawing upon the contents of the manuscript, provides a detailed analysis of the grammatical changes that have occurred in Tzotzil over the past four centuries.
Seizures in Alzheimer Disease: Clinical and Epidemiological Data
Authors: Pandis Dionysios , Scarmeas Nikolaos
Published: October 2012

Alzheimer disease (AD) and epilepsy are disorders commonly seen in the elderly. Many studies have shown that patients with AD are at increased risk for developing seizures and epilepsy. Whereas, patients with specific types of epilepsy, such as temporal lobe epilepsy (TLE), experience some degree of cognitive dysfunction, questions have been raised as to whether these disorders share some underlying pathophysiologic mechanisms or whether one is an epiphenomenon of the other. In this report, we review some of the available clinical and epidemiologic literature on various aspects of the topic of seizures in AD, including seizure rates and types, risk factors for seizures, electroencephalographic findings, treatment options, limitations, and methodological issues. Overall, multiple aspects of the literature on seizures and epilepsy in AD, including diagnosis, risk factors, the role of EEG in diagnosis, and the response to treatment are not clear and suffer from many methodological limitations and gaps.