bluuebunny/crossref_metadata_2025 huggingface dataset:
Patterns and determinants of acute psychiatric readmissions
Authors: Behr Graham Michael , Christie Cathy , Soderlund Neil , Lee Tennyson
Published: June 2017

Objectives . Deinstitutionalisation and shortage of psychiatric beds worldwide has led to extensive research into the risk fac- tors and interventions associated with rapid and recurrent admission to hospital. Little research of this nature has taken place in South Africa, particularly with regard to acute hospital admissions. This study attempted primarily to assess the effect of length of stay and administration of depot antipsychotics in hospital on time to readmission.Design. A retrospective cohort of 180 admissions was fol- lowed up for 12 months, after an index discharge, by means of multiple hospital and community-based record reviews. Each readmission was analysed as an event using a survival analysis model. Setting. Chris Hani Baragwanath Hospital, Gauteng.Subjects. A random sample of patients admitted during a 6-month period in 1996. Outcome measures. Time to readmission.Results. Two hundred and eighty-four admissions were analysed. The only factor that provided a significant protective effect was being married or cohabiting ( P = 0.015). Clinic attendance showed a slight protective effect early on but con- ferred a significantly higher risk of readmission on those who had been out of hospital for a long period ( P = 0.001). Only 21% of discharged patients ever attended a clinic. The overall risk of readmission was significantly higher in the first 90 days post discharge. Conclusions. The lack of impact of length of hospital stay and use of depot neuroleptics on time to readmission may indicate that patients are being kept for appropriate duration and that the most ill patients are receiving depot medication. Several sampling and statistical artefacts may explain some of our findings. These results confirm the worldwide difficulty in finding consistent and accurate predictors of readmission. Low rates of successful referral to community aftercare need to be addressed before their effectiveness can be reasonably assessed. The inherent instability of the post-discharge period is a potential area for further investigation and intensive management.
Fluvoxamine in the treatment of trichotillomania, obsessive compulsive disorder and major depression - a case report
Authors: Verster R , Matjane M
Published: June 2017

NO ABSTRACT AVAILIBLE
Active Tuberculosis Case Finding in Haiti
Authors: Masur Jack , Koenig Serena P. , Julma Pierrot , Ocheretina Oksana , Durán-Mendicuti Maria A. , Fitzgerald Daniel W. , Pape Jean W.
Published: June 2017

In 2010, Haiti suffered from a devastating earthquake; data on the impact on the tuberculosis (TB) epidemic are limited. From January to June 2013, we conducted active case finding at the household level in a slum in Port-au-Prince. Community health workers identified individuals with cough ≥ 2 weeks, and referred them for evaluation. Contact tracing was conducted for patients with active TB. Of an estimated 7,500 residents screened, 394 (5%) had cough and were tested for TB. One hundred (25%) were diagnosed with active TB; 53 (53%) were smear positive. Ninety of these TB index cases provided 317 contacts, and 44 (14%) were diagnosed with active TB; 17 (39%) were smear positive. Overall, 144 TB cases were detected in 6 months (1,920/100,000; national estimate 200/100,000). We found a high burden of undiagnosed TB in Port-au-Prince 3 years after the earthquake. Further assessment of the burden of TB is indicated.
Regional severe particle pollution and its association with synoptic weather patterns in the Yangtze River Delta region, China
Authors: Shu Lei , Xie Min , Gao Da , Wang Tijian , Fang Dexian , Liu Qian , Huang Anning , Peng Liwen
Published: June 2017

Abstract. Regional air pollution is significantly associated with the dominant weather systems. In this study, the relationship between the particle pollution over the Yangtze River Delta (YRD) region and the weather patterns is investigated. Firstly, the pollution characteristics of particles (PM2.5 and PM10) in YRD are studied by using the in situ monitoring data in 16 cities from December 2013 to November 2014. The results show that the annual average concentrations in the cities of Jiangsu Province all exceed the national air quality standard. The pollution level is higher in the inland areas. Highest values can be found in Nanjing, with the concentrations of PM2.5 and PM10 being 79 μg · m−3 and 130  μg · m−3, respectively. The PM2.5/PM10 ratios are usually high in YRD, indicating that PM2.5 is the overwhelmingly dominant particle pollutant. The wintertime peak of particle concentrations is tightly linked to the increased emissions in the heating season and the poor meteorological condition. Secondly, based on NCEP reanalysis data, synoptic weather classification is conducted to reveal that the weather patterns are easy to cause heavy pollution in YRD. Five typical synoptic patterns are objectively identified, including the East Asian trough rear pattern, the depression inverted trough pattern, the transversal trough pattern, the high-pressure controlled pattern, and the northeast cold vortex pattern. Finally, synthetic analysis of meteorological fields and backward trajectory calculation are used to further clarify how these patterns impact particle concentrations. It is clarified that YRD is largely influenced by polluted air masses from the northern and the southern inland areas when it is at the rear of the East Asian major trough. In this case, the strong northwest wind hinders the vertical outward transport of pollutants. Thus, the East Asian trough rear pattern is quite favorable for the accumulation of pollutants in YRD, and respectively contributes 70.4 % and 78.3 % to the occurrence of large-scale regional PM2.5 and PM10 pollution episodes. While under the weather systems for other patterns, the clean marine air masses may play great roles in the mitigation of particle pollution in YRD. The correlation between weather patterns and particle pollution can provide valuable views in the decision-making on pollution control and mitigation strategies.
Coastal sources, sinks and strong organic complexation of dissolved cobalt within the US North Atlantic GEOTRACES transect GA03
Authors: Noble Abigail E. , Ohnemus Daniel C. , Hawco Nicholas J. , Lam Phoebe J. , Saito Mak A.
Published: June 2017

Abstract. Cobalt is the scarcest of metallic micronutrients and displays a complex biogeochemical cycle. This study examines the distribution, chemical speciation, and biogeochemistry of dissolved cobalt during the US North Atlantic GEOTRACES transect expeditions (GA03/3_e), which took place in the fall of 2010 and 2011. Two major subsurface sources of cobalt to the North Atlantic were identified. The more prominent of the two was a large plume of cobalt emanating from the African coast off the eastern tropical North Atlantic coincident with the oxygen minimum zone (OMZ) likely due to reductive dissolution, biouptake and remineralization, and aeolian dust deposition. The occurrence of this plume in an OMZ with oxygen above suboxic levels implies a high threshold for persistence of dissolved cobalt plumes. The other major subsurface source came from Upper Labrador Seawater, which may carry high cobalt concentrations due to the interaction of this water mass with resuspended sediment at the western margin or from transport further upstream. Minor sources of cobalt came from dust, coastal surface waters and hydrothermal systems along the Mid-Atlantic Ridge. The full depth section of cobalt chemical speciation revealed near-complete complexation in surface waters, even within regions of high dust deposition. However, labile cobalt observed below the euphotic zone demonstrated that strong cobalt-binding ligands were not present in excess of the total cobalt concentration there, implying that mesopelagic labile cobalt was sourced from the remineralization of sinking organic matter. In the upper water column, correlations were observed between total cobalt and phosphate, and between labile cobalt and phosphate, demonstrating a strong biological influence on cobalt cycling. Along the western margin off the North American coast, this correlation with phosphate was no longer observed and instead a relationship between cobalt and salinity was observed, reflecting the importance of coastal input processes on cobalt distributions. In deep waters, both total and labile cobalt concentrations were lower than in intermediate depth waters, demonstrating that scavenging may remove labile cobalt from the water column. Total and labile cobalt distributions were also compared to a previously published South Atlantic GEOTRACES-compliant zonal transect (CoFeMUG, GAc01) to discern regional biogeochemical differences. Together, these Atlantic sectional studies highlight the dynamic ecological stoichiometry of total and labile cobalt. As increasing anthropogenic use and subsequent release of cobalt poses the potential to overpower natural cobalt signals in the oceans, it is more important than ever to establish a baseline understanding of cobalt distributions in the ocean.
Role of forcing uncertainty and background model error characterization in snow data assimilation
Authors: Kumar Sujay V. , Dong Jiarui , Peters-Lidard Christa D. , Mocko David , Gómez Breogán
Published: June 2017

Abstract. Accurate specification of the model error covariances in data assimilation systems is a challenging issue. Ensemble land data assimilation methods rely on stochastic perturbations of input forcing and model prognostic fields for developing representations of input model error covariances. This article examines the limitations of using a single forcing dataset for specifying forcing uncertainty inputs for assimilating snow depth retrievals. Using an idealized data assimilation experiment, the article demonstrates that the use of hybrid forcing input strategies (either through the use of an ensemble of forcing products or through the added use of the forcing climatology) provide a better characterization of the background model error, which leads to improved data assimilation results, especially during the snow accumulation and melt-time periods. The use of hybrid forcing ensembles is then employed for assimilating snow depth retrievals from the AMSR2 instrument over two domains in the continental USA with different snow evolution characteristics. Over a region near the Great Lakes, where the snow evolution tends to be ephemeral, the use of hybrid forcing ensembles provides significant improvements relative to the use of a single forcing dataset. Over the Colorado headwaters characterized by large snow accumulation, the impact of using the forcing ensemble is less prominent and is largely limited to the snow transition time periods. The results of the article demonstrate that improving the background model error through the use of a forcing ensemble enables the assimilation system to better incorporate the observational information.
VISUAL TRACKING UTILIZING OBJECT CONCEPT FROM DEEP LEARNING NETWORK
Authors: Xiao C. , Yilmaz A. , Lia S.
Published: June 2017

Abstract. Despite having achieved good performance, visual tracking is still an open area of research, especially when target undergoes serious appearance changes which are not included in the model. So, in this paper, we replace the appearance model by a concept model which is learned from large-scale datasets using a deep learning network. The concept model is a combination of high-level semantic information that is learned from myriads of objects with various appearances. In our tracking method, we generate the target’s concept by combining the learned object concepts from classification task. We also demonstrate that the last convolutional feature map can be used to generate a heat map to highlight the possible location of the given target in new frames. Finally, in the proposed tracking framework, we utilize the target image, the search image cropped from the new frame and their heat maps as input into a localization network to find the final target position. Compared to the other state-of-the-art trackers, the proposed method shows the comparable and at times better performance in real-time.
ARCHETYPAL ANALYSIS FOR SPARSE REPRESENTATION-BASED HYPERSPECTRAL SUB-PIXEL QUANTIFICATION
Authors: Drees L. , Roscher R.
Published: June 2017

Abstract. This paper focuses on the quantification of land cover fractions in an urban area of Berlin, Germany, using simulated hyperspectral EnMAP data with a spatial resolution of 30m×30m. For this, sparse representation is applied, where each pixel with unknown surface characteristics is expressed by a weighted linear combination of elementary spectra with known land cover class. The elementary spectra are determined from image reference data using simplex volume maximization, which is a fast heuristic technique for archetypal analysis. In the experiments, the estimation of class fractions based on the archetypal spectral library is compared to the estimation obtained by a manually designed spectral library by means of reconstruction error, mean absolute error of the fraction estimates, sum of fractions and the number of used elementary spectra. We will show, that a collection of archetypes can be an adequate and efficient alternative to the spectral library with respect to mentioned criteria.
DETECTION OF CITRUS TREES FROM UAV DSMS
Authors: Ok A. O. , Ozdarici-Ok A.
Published: June 2017

Abstract. This paper presents an automated approach to detect citrus trees from digitals surface models (DSMs) as a single source. The DSMs in this study are generated from Unmanned Aerial Vehicles (UAVs), and the proposed approach first considers the symmetric nature of the citrus trees, and it computes the orientation-based radial symmetry in an efficient way. The approach also takes into account the local maxima (LM) information to verify the output of the radial symmetry. Our contributions in this study are twofold: (i) Such an integrated approach (symmetry + LM) has not been tested to detect (citrus) trees (in orchards), and (ii) the validity of such an integrated approach has not been experienced for an input, e.g. a single DSM. Experiments are performed on five test patches. The results reveal that our approach is capable of counting most of the citrus trees without manual intervention. Comparison to the state-of-the-art reveals that the proposed approach provides notable detection performance by providing the best balance between precision and recall measures.
Monitoring of Building Construction by 4D Change Detection Using Multi-temporal SAR Images
Authors: Yang C. H. , Pang Y. , Soergel U.
Published: June 2017

Abstract. Monitoring urban changes is important for city management, urban planning, updating of cadastral map, etc. In contrast to conventional field surveys, which are usually expensive and slow, remote sensing techniques are fast and cost-effective alternatives. Spaceborne synthetic aperture radar (SAR) sensors provide radar images captured rapidly over vast areas at fine spatiotemporal resolution. In addition, the active microwave sensors are capable of day-and-night vision and independent of weather conditions. These advantages make multi-temporal SAR images suitable for scene monitoring. Persistent scatterer interferometry (PSI) detects and analyses PS points, which are characterized by strong, stable, and coherent radar signals throughout a SAR image sequence and can be regarded as substructures of buildings in built-up cities. Attributes of PS points, for example, deformation velocities, are derived and used for further analysis. Based on PSI, a 4D change detection technique has been developed to detect disappearance and emergence of PS points (3D) at specific times (1D). In this paper, we apply this 4D technique to the centre of Berlin, Germany, to investigate its feasibility and application for construction monitoring. The aims of the three case studies are to monitor construction progress, business districts, and single buildings, respectively. The disappearing and emerging substructures of the buildings are successfully recognized along with their occurrence times. The changed substructures are then clustered into single construction segments based on DBSCAN clustering and α-shape outlining for object-based analysis. Compared with the ground truth, these spatiotemporal results have proven able to provide more detailed information for construction monitoring.
CHAIN-WISE GENERALIZATION OF ROAD NETWORKS USING MODEL SELECTION
Authors: Bulatov D. , Wenzel S. , Häufel G. , Meidow J.
Published: June 2017

Abstract. Streets are essential entities of urban terrain and their automatized extraction from airborne sensor data is cumbersome because of a complex interplay of geometric, topological and semantic aspects. Given a binary image, representing the road class, centerlines of road segments are extracted by means of skeletonization. The focus of this paper lies in a well-reasoned representation of these segments by means of geometric primitives, such as straight line segments as well as circle and ellipse arcs. We propose the fusion of raw segments based on similarity criteria; the output of this process are the so-called chains which better match to the intuitive perception of what a street is. Further, we propose a two-step approach for chain-wise generalization. First, the chain is pre-segmented using circlePeucker and finally, model selection is used to decide whether two neighboring segments should be fused to a new geometric entity. Thereby, we consider both variance-covariance analysis of residuals and model complexity. The results on a complex data-set with many traffic roundabouts indicate the benefits of the proposed procedure.
DTM GENERATION FROM TERRASAR-X USING TIN ALGORITHM IN PAPUA ISLAND, INDONESIA
Authors: Susetyo D. B. , Syafiudin M. F. , Prasetyo Y.
Published: June 2017

Abstract. One of the outputs of mapping activity in Indonesia is Digital Terrain Model (DTM). DTM generated by stereo plotting with photogrammetry concept, where Indonesia Topography Map at medium scale using Synthetic Aperture Radar (SAR), and currently, one of SAR data that used to produce Indonesian Topographic Map is TerraSAR-X. This paper discusses about DTM generation in Papua Island, Indonesia, using TerraSAR-X, which is part of topographic mapping activity on a scale of 1 : 25,000. We choose Triangulated Irregular Network (TIN) as the interpolation method. After TIN was build and edited, we have to check to produce good DTM. Quality control involves visual and statistic quality. In statistic aspect, we compare Linear Error 90 % (LE90) value to map accuracy that regulated in Head of Geospatial Information Agency Rules Number 15 Year 2014. We use 50 test points for 59 map sheets in scale 1 : 25,000 (the area around 10,000 km2). To validate the elevation, we interpret test points elevation in the stereo model, then we compare to an elevation in DTM. LE90 value is 9.75 m, so we can conclude that DTM elevation still in class 3. In a visual aspect, we must edit the DTM. There are 9 parameters in visual quality control, and to meets these parameters, we can use three methods: add and reduce mass point, move mass point, and add breakline. Editing to the DTM can make we sure that it meets the quality standard in scale 1 : 25,000 data.
WEAKLY SUPERVISED SEGMENTATION-AIDED CLASSIFICATION OF URBAN SCENES FROM 3D LIDAR POINT CLOUDS
Authors: Guinard S. , Landrieu L.
Published: June 2017

Abstract. We consider the problem of the semantic classification of 3D LiDAR point clouds obtained from urban scenes when the training set is limited. We propose a non-parametric segmentation model for urban scenes composed of anthropic objects of simple shapes, partionning the scene into geometrically-homogeneous segments which size is determined by the local complexity. This segmentation can be integrated into a conditional random field classifier (CRF) in order to capture the high-level structure of the scene. For each cluster, this allows us to aggregate the noisy predictions of a weakly-supervised classifier to produce a higher confidence data term. We demonstrate the improvement provided by our method over two publicly-available large-scale data sets.
BUILDING DETECTION USING AERIAL IMAGES AND DIGITAL SURFACE MODELS
Authors: Mu J. , Cui S. , Reinartz P.
Published: June 2017

Abstract. In this paper a method for building detection in aerial images based on variational inference of logistic regression is proposed. It consists of three steps. In order to characterize the appearances of buildings in aerial images, an effective bag-of-Words (BoW) method is applied for feature extraction in the first step. In the second step, a classifier of logistic regression is learned using these local features. The logistic regression can be trained using different methods. In this paper we adopt a fully Bayesian treatment for learning the classifier, which has a number of obvious advantages over other learning methods. Due to the presence of hyper prior in the probabilistic model of logistic regression, approximate inference methods have to be applied for prediction. In order to speed up the inference, a variational inference method based on mean field instead of stochastic approximation such as Markov Chain Monte Carlo is applied. After the prediction, a probabilistic map is obtained. In the third step, a fully connected conditional random field model is formulated and the probabilistic map is used as the data term in the model. A mean field inference is utilized in order to obtain a binary building mask. A benchmark data set consisting of aerial images and digital surfaced model (DSM) released by ISPRS for 2D semantic labeling is used for performance evaluation. The results demonstrate the effectiveness of the proposed method.
VEGETATION ANALYSIS AND LAND USE LAND COVER CLASSIFICATION OF FOREST IN UTTARA KANNADA DISTRICT INDIA THROUGH GEO-INFORMATICS APPROACH
Authors: Koppad A. G. , Janagoudar B. S.
Published: June 2017

Abstract. The study was conducted in Uttara Kannada districts during the year 2012–2014. The study area lies between 13.92° N to 15.52° N latitude and 74.08° E to 75.09° E longitude with an area of 10,215 km2. The Indian satellite IRS P6 LISS-III imageries were used to classify the land use land cover classes with ground truth data collected with GPS through supervised classification in ERDAS software. The land use and land cover classes identified were dense forest, horticulture plantation, sparse forest, forest plantation, open land and agriculture land. The dense forest covered an area of 63.32 % (6468.70 sq km) followed by agriculture 12.88 % (1315.31 sq. km), sparse forest 10.59 % (1081.37 sq. km), open land 6.09 % (622.37 sq. km), horticulture plantation and least was forest plantation (1.07 %). Settlement, stony land and water body together cover about 4.26 percent of the area. The study indicated that the aspect and altitude influenced the forest types and vegetation pattern. The NDVI map was prepared which indicated that healthy vegetation is represented by high NDVI values between 0.1 and 1. The non-vegetated features such as water bodies, settlement, and stony land indicated less than 0.1 values. The decrease in forest area in some places was due to anthropogenic activities. The thematic map of land use land cover classes was prepared using Arc GIS Software.
LAND USE/LAND COVER CHANGES IN SEMI-ARID MOUNTAIN LANDSCAPE IN SOUTHERN INDIA: A GEOINFORMATICS BASED MARKOV CHAIN APPROACH
Authors: Rahaman S. A. , Aruchamy S. , Balasubramani K. , Jegankumar R.
Published: June 2017

Abstract. Nowadays land use/ land cover in mountain landscape is in critical condition; it leads to high risky and uncertain environments. These areas are facing multiple stresses including degradation of land resources; vagaries of climate and depletion of water resources continuously affect land use practices and livelihoods. To understand the Land use/Land cover (Lu/Lc) changes in a semi-arid mountain landscape, Kallar watershed of Bhavani basin, in southern India has been chosen. Most of the hilly part in the study area covers with forest, plantation, orchards and vegetables and which are highly affected by severe soil erosion, landslide, frequent rainfall failures and associated drought. The foothill regions are mainly utilized for agriculture practices; due to water scarcity and meagre income, the productive agriculture lands are converted into settlement plots and wasteland. Hence, land use/land cover change deduction; a stochastic processed based method is indispensable for future prediction. For identification of land use/land cover, and vegetation changes, Landsat TM, ETM (1995, 2005) and IRS P6- LISS IV (2015) images were used. Through CAMarkov chain analysis, Lu/Lc changes in past three decades (1995, 2005, and 2015) were identified and projected for (2020 and 2025); Normalized Difference Vegetation Index (NDVI) were used to find the vegetation changes. The result shows that, maximum changes occur in the plantation and slight changes found in forest cover in the hilly terrain. In foothill areas, agriculture lands were decreased while wastelands and settlement plots were increased. The outcome of the results helps to farmer and policy makers to draw optimal lands use planning and better management strategies for sustainable development of natural resources.
MULTI-SCALE SEGMENTATION OF HIGH RESOLUTION REMOTE SENSING IMAGES BY INTEGRATING MULTIPLE FEATURES
Authors: Di Y. , Jiang G. , Yan L. , Liu H. , Zheng S.
Published: June 2017

Abstract. Most of multi-scale segmentation algorithms are not aiming at high resolution remote sensing images and have difficulty to communicate and use layers’ information. In view of them, we proposes a method of multi-scale segmentation of high resolution remote sensing images by integrating multiple features. First, Canny operator is used to extract edge information, and then band weighted distance function is built to obtain the edge weight. According to the criterion, the initial segmentation objects of color images can be gained by Kruskal minimum spanning tree algorithm. Finally segmentation images are got by the adaptive rule of Mumford–Shah region merging combination with spectral and texture information. The proposed method is evaluated precisely using analog images and ZY-3 satellite images through quantitative and qualitative analysis. The experimental results show that the multi-scale segmentation of high resolution remote sensing images by integrating multiple features outperformed the software eCognition fractal network evolution algorithm (highest-resolution network evolution that FNEA) on the accuracy and slightly inferior to FNEA on the efficiency.
UNMIXING-BASED DENOISING AS A PRE-PROCESSING STEP FOR CORAL REEF ANALYSIS
Authors: Cerra D. , Traganos D. , Gege P. , Reinartz P.
Published: June 2017

Abstract. Coral reefs, among the world’s most biodiverse and productive submerged habitats, have faced several mass bleaching events due to climate change during the past 35 years. In the course of this century, global warming and ocean acidification are expected to cause corals to become increasingly rare on reef systems. This will result in a sharp decrease in the biodiversity of reef communities and carbonate reef structures. Coral reefs may be mapped, characterized and monitored through remote sensing. Hyperspectral images in particular excel in being used in coral monitoring, being characterized by very rich spectral information, which results in a strong discrimination power to characterize a target of interest, and separate healthy corals from bleached ones. Being submerged habitats, coral reef systems are difficult to analyse in airborne or satellite images, as relevant information is conveyed in bands in the blue range which exhibit lower signal-to-noise ratio (SNR) with respect to other spectral ranges; furthermore, water is absorbing most of the incident solar radiation, further decreasing the SNR. Derivative features, which are important in coral analysis, result greatly affected by the resulting noise present in relevant spectral bands, justifying the need of new denoising techniques able to keep local spatial and spectral features. In this paper, Unmixing-based Denoising (UBD) is used to enable analysis of a hyperspectral image acquired over a coral reef system in the Red Sea based on derivative features. UBD reconstructs pixelwise a dataset with reduced noise effects, by forcing each spectrum to a linear combination of other reference spectra, exploiting the high dimensionality of hyperspectral datasets. Results show clear enhancements with respect to traditional denoising methods based on spatial and spectral smoothing, facilitating the coral detection task.
VARIABLE SELECTION FOR ROAD SEGMENTATION IN AERIAL IMAGES
Authors: Warnke S. , Bulatov D.
Published: June 2017

Abstract. For extraction of road pixels from combined image and elevation data, Wegner et al. (2015) proposed classification of superpixels into road and non-road, after which a refinement of the classification results using minimum cost paths and non-local optimization methods took place. We believed that the variable set used for classification was to a certain extent suboptimal, because many variables were redundant while several features known as useful in Photogrammetry and Remote Sensing are missed. This motivated us to implement a variable selection approach which builds a model for classification using portions of training data and subsets of features, evaluates this model, updates the feature set, and terminates when a stopping criterion is satisfied. The choice of classifier is flexible; however, we tested the approach with Logistic Regression and Random Forests, and taylored the evaluation module to the chosen classifier. To guarantee a fair comparison, we kept the segment-based approach and most of the variables from the related work, but we extended them by additional, mostly higher-level features. Applying these superior features, removing the redundant ones, as well as using more accurately acquired 3D data allowed to keep stable or even to reduce the misclassification error in a challenging dataset.
CHANGE DETECTION OF REMOTE SENSING IMAGES BY DT-CWT AND MRF
Authors: Ouyang S. , Fan K. , Wang H. , Wang Z.
Published: June 2017

Abstract. Aiming at the significant loss of high frequency information during reducing noise and the pixel independence in change detection of multi-scale remote sensing image, an unsupervised algorithm is proposed based on the combination between Dual-tree Complex Wavelet Transform (DT-CWT) and Markov random Field (MRF) model. This method first performs multi-scale decomposition for the difference image by the DT-CWT and extracts the change characteristics in high-frequency regions by using a MRF-based segmentation algorithm. Then our method estimates the final maximum a posterior (MAP) according to the segmentation algorithm of iterative condition model (ICM) based on fuzzy c-means(FCM) after reconstructing the high-frequency and low-frequency sub-bands of each layer respectively. Finally, the method fuses the above segmentation results of each layer by using the fusion rule proposed to obtain the mask of the final change detection result. The results of experiment prove that the method proposed is of a higher precision and of predominant robustness properties.
THE CORRELATION ANALYSIS OF SUBSIDENCE MONITORING BY D-INSAR AND THE CHANGE OF URBAN CONSTRUCTION LAND
Authors: Yang K. J. , Jiang X. X. , Cao L. , Lei F. , Zeng H. , Zhang Z.
Published: June 2017

Abstract. The change of urban construction land affect the subsidence directly or indirectly, the method of D-InSAR has centimeter level or even millimeter accuracy that can provide a reliable and accurate data for the research of correlation analysis of subsidence monitoring by D-InSAR and the change of urban construction land. This article takes Guiyang, Nanning city as example, using 3m level TerraSAR data to construct the Subsidence model by interferometric measurement, then compared with the Chinese national land use change remote sensing survey database at the same measure time to have a correlation analysis GIS research between subsidence and the change of urban construction land. The results shows that the integral correlation coefficient achieved 0.78 between subsidence and the change of urban construction land, the major construction area and the high density construction area are with severe land subsidence. In addition, the correlation coefficient increased from the main city to the suburbs, indicates that some of the main city causes permanent settlement and is difficult to recover. It also shows that some area subsidence caused by long-term mining or other natural factors has no strong correlation with the change of urban construction land, therefore, the results of D-InSAR subsidence monitoring have a reaction on urban construction planning, guiding urban planning to high stability, low settlement area.
BUILDING ROOF BOUNDARY EXTRACTION FROM LiDAR AND IMAGE DATA BASED ON MARKOV RANDOM FIELD
Authors: Dal Poz A. P. , Fernandes V. J. M.
Published: June 2017

Abstract. In this paper a method for automatic extraction of building roof boundaries is proposed, which combines LiDAR data and highresolution aerial images. The proposed method is based on three steps. In the first step aboveground objects are extracted from LiDAR data. Initially a filtering algorithm is used to process the original LiDAR data for getting ground and non-ground points. Then, a region-growing procedure and the convex hull algorithm are sequentially used to extract polylines that represent aboveground objects from the non-ground point cloud. The second step consists in extracting corresponding LiDAR-derived aboveground objects from a high-resolution aerial image. In order to avoid searching for the interest objects over the whole image, the LiDAR-derived aboveground objects’ polylines are photogrammetrically projected onto the image space and rectangular bounding boxes (sub-images) that enclose projected polylines are generated. Each sub-image is processed for extracting the polyline that represents the interest aboveground object within the selected sub-image. Last step consists in identifying polylines that represent building roof boundaries. We use the Markov Random Field (MRF) model for modelling building roof characteristics and spatial configurations. Polylines that represent building roof boundaries are found by optimizing the resulting MRF energy function using the Genetic Algorithm. Experimental results are presented and discussed in this paper.
GF-7 IMAGING SIMULATION AND DSM ACCURACY ESTIMATE
Authors: Yue Q. , Tang X. , Gao X.
Published: June 2017

Abstract. GF-7 satellite is a two-line-array stereo imaging satellite for surveying and mapping which will be launched in 2018. Its resolution is about 0.8 meter at subastral point corresponding to a 20 km width of cloth, and the viewing angle of its forward and backward cameras are 5 and 26 degrees. This paper proposed the imaging simulation method of GF-7 stereo images. WorldView-2 stereo images were used as basic data for simulation. That is, we didn’t use DSM and DOM as basic data (we call it “ortho-to-stereo” method) but used a “stereo-to-stereo” method, which will be better to reflect the difference of geometry and radiation in different looking angle. The shortage is that geometric error will be caused by two factors, one is different looking angles between basic image and simulated image, another is not very accurate or no ground reference data. We generated DSM by WorldView-2 stereo images. The WorldView-2 DSM was not only used as reference DSM to estimate the accuracy of DSM generated by simulated GF-7 stereo images, but also used as “ground truth” to establish the relationship between WorldView-2 image point and simulated image point. Static MTF was simulated on the instantaneous focal plane “image” by filtering. SNR was simulated in the electronic sense, that is, digital value of WorldView-2 image point was converted to radiation brightness and used as radiation brightness of simulated GF-7 camera. This radiation brightness will be converted to electronic number n according to physical parameters of GF-7 camera. The noise electronic number n1 will be a random number between -√n and √n. The overall electronic number obtained by TDI CCD will add and converted to digital value of simulated GF-7 image. Sinusoidal curves with different amplitude, frequency and initial phase were used as attitude curves. Geometric installation errors of CCD tiles were also simulated considering the rotation and translation factors. An accuracy estimate was made for DSM generated from simulated images.
STRESS MONITORING OF MULBERRY PLANTS BY FINDING REP USING HYPERSPECTRAL DATA
Authors: Bhosle K. , Musande V.
Published: June 2017

Abstract. Stressed on crop can be monitored using different indices. Red Edge position is good estimator for stress monitoring. The red edge position (REP) is strongly correlated with foliar chlorophyll content. Strong chlorophyll absorption causes the abrupt change in the 680–800 nm region of reflectance spectra of green vegetation. The red edge consist the point of maximum slope between red and near-infrared wavelengths. REP can be used to recognize green zone of the observation area. The REP is present in spectra for vegetation recorded by remote sensing methods. REP is clearer and significant in hyper spectral data as hyper spectral consist of more and continuous bands data. In this paper experiments were carried out for mulberry crop using USGS EO-1 Hyperion data. Atmospheric corrected data is used for classification. Classification is carried out on small cluster of 14 field samples. Ground truth is verified and classified by comparing with Hyperion data. REP is different for different stressed condition of crops and shows healthy and diseased crop condition. Nutritional stresses, diseases, drought of plants are detected using REP. Stressed and healthy field of mulberry are estimated by calculating REP using maximum first derivative, linear interpolation, linear extrapolation method. Finally REP is compared using above methods. It is noticeable difference of REP for healthy and stressed crop. The research indicates that overall accuracy using maximum first derivative was 92.85 % and it was more compared to other methods. Linear extrapolation gives less accuracy compared to linear interpolation method.
A DETECTION METHOD OF ARTIFICIAL AREA FROM HIGH RESOLUTION REMOTE SENSING IMAGES BASED ON MULTI SCALE AND MULTI FEATURE FUSION
Authors: Li P. , Hu X. , Hu Y. , Ding Y. , Wang L. , Li L.
Published: June 2017

Abstract. In order to solve the problem of automatic detection of artificial objects in high resolution remote sensing images, a method for detection of artificial areas in high resolution remote sensing images based on multi-scale and multi feature fusion is proposed. Firstly, the geometric features such as corner, straight line and right angle are extracted from the original resolution, and the pseudo corner points, pseudo linear features and pseudo orthogonal angles are filtered out by the self-constraint and mutual restraint between them. Then the radiation intensity map of the image with high geometric characteristics is obtained by the linear inverse distance weighted method. Secondly, the original image is reduced to multiple scales and the visual saliency image of each scale is obtained by adaptive weighting of the orthogonal saliency, the local brightness and contrast which are calculated at the corresponding scale. Then the final visual saliency image is obtained by fusing all scales’ visual saliency images. Thirdly, the visual saliency images of artificial areas based on multi scales and multi features are obtained by fusing the geometric feature energy intensity map and visual saliency image obtained in previous decision level. Finally, the artificial areas can be segmented based on the method called OTSU. Experiments show that the method in this paper not only can detect large artificial areas such as urban city, residential district, but also detect the single family house in the countryside correctly. The detection rate of artificial areas reached 92 %.
A TIE POINT MATCHING STRATEGY FOR VERY HIGH RESOLUTION SAR-OPTICAL
STEREOGRAMMETY OVER URBAN AREAS
Authors: Qiu C. P. , Schmitt M. , Zhu X. X.
Published: June 2017

Abstract. This paper presents a matching strategy for sparsely distributed tie points, which can be used to carry out SAR-optical stereogrammetry. Since matching homologue points in heterogeneous multi-sensor image of complex terrain remains a challenging task, the proposed procedure combines the tie point matching step with the estimation of the corresponding 3D object coordinates. The results of this study are two-fold: On the one hand, they show that fully automatic SAR-optical stereogrammetry using very high resolution data of urban scenes is generally feasible and can provide 3D accuracies in the meter-domain. On the other hand, they highlight the difficulty of multi-sensor image matching, as well as the importance of robust matching strategies.
UNCERTAINTY ASSESSMENT AND WEIGHT MAP GENERATION FOR EFFICIENT FUSION OF TANDEM-X AND CARTOSAT-1 DEMS
Authors: Bagheri H. , Schmitt M. , Zhu X. X.
Published: June 2017

Abstract. Recently, with InSAR data provided by the German TanDEM-X mission, a new global, high-resolution Digital Elevation Model (DEM) has been produced by the German Aerospace Center (DLR) with unprecedented height accuracy. However, due to SAR-inherent sensor specifics, its quality decreases over urban areas, making additional improvement necessary. On the other hand, DEMs derived from optical remote sensing imagery, such as Cartosat-1 data, have an apparently greater resolution in urban areas, making their fusion with TanDEM-X elevation data a promising perspective. The objective of this paper is two-fold: First, the height accuracies of TanDEM-X and Cartosat-1 elevation data over different land types are empirically evaluated in order to analyze the potential of TanDEM-XCartosat- 1 DEM data fusion. After the quality assessment, urban DEM fusion using weighted averaging is investigated. In this experiment, both weight maps derived from the height error maps delivered with the DEM data, as well as more sophisticated weight maps predicted by a procedure based on artificial neural networks (ANNs) are compared. The ANN framework employs several features that can describe the height residual performance to predict the weights used in the subsequent fusion step. The results demonstrate that especially the ANN-based framework is able to improve the quality of the final DEM through data fusion.
SPLITTING TERRACED HOUSES INTO SINGLE UNITS USING OBLIQUE AERIAL IMAGERY
Authors: Dahlke D.
Published: June 2017

Abstract. This paper introduces a method to subdivide complex building structures like terraced houses into single house units comparable to units available in a cadastral map. 3D line segments are detected with sub-pixel accuracy in traditional vertical true orthomosaics as well as in innovative oblique true orthomosaics and their respective surface models. Hereby high gradient strengths on roofs as well as fac¸ades are taken into account. By investigating the coplanarity and frequencies within a set of 3D line segments, individual cut lines for a building complex are found. The resulting regions ideally describe single houses and thus the object complexity is reduced for subsequent topological, semantical or geometrical considerations. For the chosen study area with 70 buidling outlines a hit rate of 80% for cut lines is achieved.
FEATURE ORIENTATION AND POSITIONAL ACCURACY ASSESSMENT OF DIGITAL ORTHOPHOTO AND LINE MAP FOR LARGE SCALE MAPPING: THE CASE STUDY ON BAHIR DAR TOWN, ETHIOPIA
Authors: Sisay Z. G. , Besha T. , Gessesse B.
Published: June 2017

Abstract. This study used in-situ GPS data to validate the accuracy of horizontal coordinates and orientation of linear features of orthophoto and line map for Bahir Dar city. GPS data is processed using GAMIT/GLOBK and Lieca GeoOfice (LGO) in a least square sense with a tie to local and regional GPS reference stations to predict horizontal coordinates at five checkpoints. Real-Time-Kinematic GPS measurement technique is used to collect the coordinates of road centerline to test the accuracy associated with the orientation of the photogrammetric line map. The accuracy of orthophoto was evaluated by comparing with in-situ GPS coordinates and it is in a good agreement with a root mean square error (RMSE) of 12.45 cm in x- and 13.97 cm in y-coordinates, on the other hand, 6.06 cm with 95 % confidence level – GPS coordinates from GAMIT/GLOBK. Whereas, the horizontal coordinates of the orthophoto are in agreement with in-situ GPS coordinates at an accuracy of 16.71 cm and 18.98 cm in x and y-directions respectively and 11.07 cm with 95 % confidence level – GPS data is processed by LGO and a tie to local GPS network. Similarly, the accuracy of linear feature is in a good fit with in-situ GPS measurement. The GPS coordinates of the road centerline deviates from the corresponding coordinates of line map by a mean value of 9.18 cm in x- direction and −14.96 cm in y-direction. Therefore, it can be concluded that, the accuracy of the orthophoto and line map is within the national standard of error budget (~ 25 cm).
LOCALIZED SEGMENT BASED PROCESSING FOR AUTOMATIC BUILDING EXTRACTION FROM LiDAR DATA
Authors: Parida G. , Rajan K. S.
Published: June 2017

Abstract. The current methods of object segmentation and extraction and classification of aerial LiDAR data is manual and tedious task. This work proposes a technique for object segmentation out of LiDAR data. A bottom-up geometric rule based approach was used initially to devise a way to segment buildings out of the LiDAR datasets. For curved wall surfaces, comparison of localized surface normals was done to segment buildings. The algorithm has been applied to both synthetic datasets as well as real world dataset of Vaihingen, Germany. Preliminary results show successful segmentation of the buildings objects from a given scene in case of synthetic datasets and promissory results in case of real world data. The advantages of the proposed work is non-dependence on any other form of data required except LiDAR. It is an unsupervised method of building segmentation, thus requires no model training as seen in supervised techniques. It focuses on extracting the walls of the buildings to construct the footprint, rather than focussing on roof. The focus on extracting the wall to reconstruct the buildings from a LiDAR scene is crux of the method proposed. The current segmentation approach can be used to get 2D footprints of the buildings, with further scope to generate 3D models. Thus, the proposed method can be used as a tool to get footprints of buildings in urban landscapes, helping in urban planning and the smart cities endeavour.