bluuebunny/crossref_metadata_2025 huggingface dataset:
A SIMPLE CLOUD DETECTION METHOD FOR GF-1 WFV IMAGERY
Authors: Jia L. L. , Wang X. Q.
Published: September 2017

Abstract. Identification of clouds in optical images is often a necessary step toward their use. However, aimed at the cloud detection methods used on GF-1 is relatively less. In order to meet the requirement of accurate cloud detection in GF-1 WFV imagery, a new method based on the combination of band operation and spatial texture feature (BOTF) is proposed in this paper. First of all, the BOTF algorithm minimize interference of highlight surface and cloud regions by the band operation, and then distinguish between cloud area and non-cloud area with spatial texture feature. Finally, the cloud mask can be acquired by threshold segmentation method. The method was validated using scenes. The results indicate that the BOTF performs well under normal conditions, and the average overall accuracy of BOTF cloud detection is better than 90 %. The proposed method can meet the needs of routine work.
MAPPING THE SENSITIVITY OF THE PUBLIC EMOTION TO THE MOVEMENT OF STOCK MARKET VALUE: A CASE STUDY OF MANHATTAN
Authors: Kang Y. , Wang J. , Wang Y. , Angsuesser S. , Fei T.
Published: September 2017

Abstract. We examined whether emotion expressed by users in social media can be influenced by stock market index or can predict the fluctuation of the stock market index. We collected the emotion data by using face detection technology and emotion cognition services for photos uploaded to Flickr. Each face’s emotion was described in 8 dimensions the location was also recorded. An emotion score index was defined based on the combination of all 8 dimensions of emotion calculated by principal component analysis. The correlation coefficients between the stock market values and emotion scores are significant (R > 0.59 with p
NIGHT TIME LIGHT SATELLITE DATA FOR EVALUATING THE SOCIOECONOMICS IN CENTRAL ASIA
Authors: Li S. , Zhang T. , Yang Z. , Li X. , Xu H.
Published: September 2017

Abstract. Using nighttime lights data combined with LandScan population counts and socioeconomic statistics, dynamic change was monitored in the social economy of the five countries in Central Asia, from 1993 to 2012. In addition, the spatial pattern of regional historical development was analyzed, using this data. The countries included in this study were Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan and Turkmenistan. The economic development in these five Central Asian countries, the movement of the economic center, the distribution of poor areas and the night light development index (NLDI) were studied at a relatively fine spatial scale. In addition, we studied the relationship between the per capita lighting and per capita GDP at the national scale, finding that the per capital lighting correlated with per capita GDP. The results of this study reflect the socioeconomic development of Central Asia but more importantly, show that nighttime light satellite images are an effective tool for monitoring spatial and temporal social economic parameters.
CORRECTION OF MEASURED TAXICAB EXHAUST EMISSION DATA BASED ON CMEM MODLE
Authors: Li Q. , Jia T.
Published: September 2017

Abstract. Carbon dioxide emissions from urban road traffic mainly come from automobile exhaust. However, the carbon dioxide emissions obtained by the instruments are unreliable due to time delay error. In order to improve the reliability of data, we propose a method to correct the measured vehicles’ carbon dioxide emissions from instrument based on the CMEM model. Firstly, the synthetic time series of carbon dioxide emissions are simulated by CMEM model and GPS velocity data. Then, taking the simulation data as the control group, the time delay error of the measured carbon dioxide emissions can be estimated by the asynchronous correlation analysis, and the outliers can be automatically identified and corrected using the principle of DTW algorithm. Taking the taxi trajectory data of Wuhan as an example, the results show that (1) the correlation coefficient between the measured data and the control group data can be improved from 0.52 to 0.59 by mitigating the systematic time delay error. Furthermore, by adjusting the outliers which account for 4.73 % of the total data, the correlation coefficient can raise to 0.63, which suggests strong correlation. The construction of low carbon traffic has become the focus of the local government. In order to respond to the slogan of energy saving and emission reduction, the distribution of carbon emissions from motor vehicle exhaust emission was studied. So our corrected data can be used to make further air quality analysis.
A NOVEL METHOD OF CLOUD AND SHADOW EXTRACTION WITH MULTI-TEMPORAL LANDSAT8 OLI IMAGES
Authors: Ni Y. , He G. , Jiang W.
Published: September 2017

Abstract. Cloud and Shadow removal is a significant step in remote sensing image process. As we all know, the ground object coverage type of the same area of the remote sensing image has little change in the short term. But for cloud and shadow coverage areas, the ground object coverage type has large change. Therefore, according to the difference between the two Landsat / OLI images caused by changes in the cover, this paper presents a method of extracting clouds and shadows based on differences in luminance values. This method selects two thresholds for the difference of brightness values, and extracts the clouds and shadows respectively, and validates them with random point method, which can obtain high precision of extracting cloud and shadow and satisfy the actual application needs.
INFLUENCE ANALYSIS OF WATERLOGGING BASED ON DEEP LEARNING MODEL IN WUHAN
Authors: Pan Y. , Shao Z. , Cheng T. , Wang Z. , Zhang Z.
Published: September 2017

Abstract. This paper analyses a large number of factors related to the influence degree of urban waterlogging in depth, and constructs the Stack Autoencoder model to explore the relationship between the waterlogging points’ influence degree and their surrounding spatial data, which will be used to realize the comprehensive analysis in the waterlogging influence on the work and life of residents. According to the data of rainstorm waterlogging in 2016 July in Wuhan, the model is validated. The experimental results show that the model has higher accuracy than the traditional linear regression model. Based on the experimental model and waterlogging points distribution information in Wuhan over the years, the influence degree of different waterlogging points can be quantitatively described, which will be beneficial to the formulation of urban flood control measures and provide a reference for the design of city drainage pipe network.
SPATIOTEMPORAL DATA ORGANIZATION AND APPLICATION RESEARCH
Authors: Tan C. , Yan S.
Published: September 2017

Abstract. Organization and management of spatiotemporal data is a key support technology for intelligence in all fields of the smart city. The construction of a smart city cannot be realized without spatiotemporal data. Oriented to support intelligent applications，this paper proposes an organizational model for spatiotemporal data, and details the construction of a spatiotemporal big data calculation, analysis, and service framework for highly efficient management and intelligent application of spatiotemporal data for the entire data life cycle.
A WEB-BASED PLATFORM FOR VISUALIZING SPATIOTEMPORAL DYNAMICS OF BIG TAXI DATA
Authors: Xiong H. , Chen L. , Gui Z.
Published: September 2017

Abstract. With more and more vehicles equipped with Global Positioning System (GPS), access to large-scale taxi trajectory data has become increasingly easy. Taxis are valuable sensors and information associated with taxi trajectory can provide unprecedented insight into many aspects of city life. But analysing these data presents many challenges. Visualization of taxi data is an efficient way to represent its distributions and structures and reveal hidden patterns in the data. However, Most of the existing visualization systems have some shortcomings. On the one hand, the passenger loading status and speed information cannot be expressed. On the other hand, mono-visualization form limits the information presentation. In view of these problems, this paper designs and implements a visualization system in which we use colour and shape to indicate passenger loading status and speed information and integrate various forms of taxi visualization. The main work as follows: 1. Pre-processing and storing the taxi data into MongoDB database. 2. Visualization of hotspots for taxi pickup points. Through DBSCAN clustering algorithm, we cluster the extracted taxi passenger’s pickup locations to produce passenger hotspots. 3. Visualizing the dynamic of taxi moving trajectory using interactive animation. We use a thinning algorithm to reduce the amount of data and design a preloading strategyto load the data smoothly. Colour and shape are used to visualize the taxi trajectory data.
A ROUGH SET DECISION TREE BASED MLP-CNN FOR VERY HIGH RESOLUTION REMOTELY SENSED IMAGE CLASSIFICATION
Authors: Zhang C. , Pan X. , Zhang S. Q. , Li H. P. , Atkinson P. M.
Published: September 2017

Abstract. Recent advances in remote sensing have witnessed a great amount of very high resolution (VHR) images acquired at sub-metre spatial resolution. These VHR remotely sensed data has post enormous challenges in processing, analysing and classifying them effectively due to the high spatial complexity and heterogeneity. Although many computer-aid classification methods that based on machine learning approaches have been developed over the past decades, most of them are developed toward pixel level spectral differentiation, e.g. Multi-Layer Perceptron (MLP), which are unable to exploit abundant spatial details within VHR images. This paper introduced a rough set model as a general framework to objectively characterize the uncertainty in CNN classification results, and further partition them into correctness and incorrectness on the map. The correct classification regions of CNN were trusted and maintained, whereas the misclassification areas were reclassified using a decision tree with both CNN and MLP. The effectiveness of the proposed rough set decision tree based MLP-CNN was tested using an urban area at Bournemouth, United Kingdom. The MLP-CNN, well capturing the complementarity between CNN and MLP through the rough set based decision tree, achieved the best classification performance both visually and numerically. Therefore, this research paves the way to achieve fully automatic and effective VHR image classification.
VISUALIZATION OF SPATIO-TEMPORAL RELATIONS IN MOVEMENT EVENT USING MULTI-VIEW
Authors: Zheng K. , Gu D. , Fang F. , Wang Y. , Liu H. , Zhao W. , Zhang M. , Li Q.
Published: September 2017

Abstract. Spatio-temporal relations among movement events extracted from temporally varying trajectory data can provide useful information about the evolution of individual or collective movers, as well as their interactions with their spatial and temporal contexts. However, the pure statistical tools commonly used by analysts pose many difficulties, due to the large number of attributes embedded in multi-scale and multi-semantic trajectory data. The need for models that operate at multiple scales to search for relations at different locations within time and space, as well as intuitively interpret what these relations mean, also presents challenges. Since analysts do not know where or when these relevant spatio-temporal relations might emerge, these models must compute statistical summaries of multiple attributes at different granularities. In this paper, we propose a multi-view approach to visualize the spatio-temporal relations among movement events. We describe a method for visualizing movement events and spatio-temporal relations that uses multiple displays. A visual interface is presented, and the user can interactively select or filter spatial and temporal extents to guide the knowledge discovery process. We also demonstrate how this approach can help analysts to derive and explain the spatio-temporal relations of movement events from taxi trajectory data.
WETLAND CHANGE DETECTION IN PROTECTED AND UNPROTECTED INDUS COASTAL AND INLAND DELTA
Authors: Ali Baig M. H. , Sultan M. , Riaz Khan M. , Zhang L. , Kozlova M. , Abbas Malik N. , Wang S.
Published: September 2017

Abstract. Worth of wetland sites lies in their ecological importance. They enhance ecosystem via provision of ecological services like improving water quality, groundwater infiltration, flood risk reduction and biodiversity regulation. Like other parts of the world Pakistan is also facing wetlands degradation. Ecological and economic significance of wetlands was recognized officially in 1971 as Pakistan became signatory of Ramsar wetland convention. Wetlands provide habitat to species of ecological and economic importance. Despite being recognized for international importance, Ramsar figures state that almost half of Pakistan’s wetlands are at moderate or prominent level threat. Wetlands ecosystems are deteriorating at a rapid rate, if uncontrolled this trend may lead to substantial losses. Therefore, management of these resources demands regular monitoring. Present study is dedicated to assessing levels of change overtime in three distinct types of wetlands in Pakistan i.e. Indus delta a coastal wetland, Uchhali complex an inland wetland which are both protected sites while another site Nurri Lagoon which is not sheltered under any category of protected areas. Remotely sensed data has remarkable applications in change detection. Multitemporal Landsat images were used to map changes occurring from 2006 to 2016. Results reveal that wetland area has considerably decreased for all types. Both protected sites have experienced degradation though impact is comparatively lesser than unprotected Nurri lagoon. Significance of protection strategies cannot be denied, it is recommended that mere declaration of a site protected area is not sufficient. It is equally important to control non-point pollutants and ensuring the compliance of conservation strategy.
EXPERIMENTAL DEM EXTRACTION FROM ASTER STEREO PAIRS AND 3D REGISTRATION BASED ON ICESAT LASER ALTIMETRY DATA IN UPSTREAM AREA OF LAMBERT GLACIER, ANTARCTICA
Authors: Hai G. , Xie H. , Chen J. , Chen L. , Li R. , Tong X.
Published: September 2017

Abstract. DEM Extraction from ASTER stereo pairs and three-dimensional registration by reference to ICESat laser altimetry data are carried out in upstream area of Lambert Glacier, East Antarctica. Since the study area is located in inland of East Antarctica where few textures exist, registration between DEM and ICESat data is performed. Firstly, the ASTER DEM generation is based on rational function model (RFM) and the procedure includes: a) rational polynomial coefficient (RPC) computation from ASTER metadata, b) L1A image product de-noise and destriping, c) local histogram equalization and matching, d) artificial collection of tie points and bundle adjustment, and e) coarse-to-fine hierarchical matching of five levels and grid matching. The matching results are filtered semi-automatically. Hereafter, DEM is interpolated using spline method with ground points converted from matching points. Secondly, the generated ASTER DEM is registered to ICESat data in three-dimensional space after Least-squares rigid transformation using singular value decomposition (SVD). The process is stated as: a) correspondence selection of terrain feature points from ICESat and DEM profiles, b) rigid transformation of generated ASTER DEM using selected feature correspondences based on least squares technique. The registration shows a good result that the elevation difference between DEM and ICESat data is low with a mean value less than 2 meters and the standard deviation around 7 meters. This DEM is generated and specially registered in Antarctic typical region without obvious ground rock control points and serves as true terrain input for further radar altimetry simulation.
INFORMATION EXTRACTION AND CHANGE ANALYSIS OF MAJOR LAKES IN TIBETAN PLATEAU BASED ON LANDSAT REMOTE SENSING IMAGES
Authors: Li Y. J. , Qiao G.
Published: September 2017

Abstract. The water resources of Tibetan plateau, particularly the lakes, has been influenced by global climate change and also reacted to global change. It is important to study the lake changes in the Tibetan plateau. This paper aimed to analyse the changes detected from remote sensing images for the typical lakes in Tibetan Plateau, including Qinghai Lake, Nam Co and Selin Co, using different information extraction methods, such as the single-band threshold method, NDWI, MNDWI, MSCT, MLC, SVM and object-oriented classification method. Results show that object-oriented classification method is better at removing ice, snow, cloud and mountain shadow, while the NDWI and MNDWI generated the worst results for lake extraction. After that, the lake's long temporal changes for Qinghai Lake, Nam Co and Selin Co derived from 12 images in different periods have been analysed using GIS technology. Results show that between 1985 and 2015, Qinghai Lake has expanded after a steady expansion; Nam Co was in the state about stable to expansion to stable. The area increased by 70.03 km2, a growth rate of 2.33 km2 a-1 and had a small change in area; Selin Co expanded after a stable state, and the area is always growing, and Selin Co's growth rate is larger than Qinghai Lake and Nam Co at the same time.
LOCAL AND GENERAL MONITORING OF FORNI GLACIER (ITALIAN ALPS) USING MULTI-PLATFORM STRUCTURE-FROM-MOTION PHOTOGRAMMETRY
Authors: Scaioni M. , Corti M. , Diolaiuti G. , Fugazza D. , Cernuschi M.
Published: September 2017

Abstract. Experts from the University of Milan have been investigating Forni Glacier in the Italian alps for decades, resulting in the archive of a cumbersome mass of observed data. While the analysis of archive maps, medium resolution satellite images and DEM’s may provide an overview of the long-term processes, the application of close-range sensing techniques offers the unprecedented opportunity to operate a 4D reconstruction of the glacier geometry at both global and local levels. In the latest years the availability of high-resolution DEM's from stereo-photogrammetry (2007) and UAV-photogrammetry (2014 and 2016) has allowed an improved analysis of the glacier ice-mass balance within time. During summer 2016 a methodology to record the local disruption processes has been investigated. The presence of vertical and sub-vertical surfaces has motivated the use of Structure-from-Motion Photogrammetry from ground-based stations, which yielded results comparable to the ones achieved using a long-range terrestrial laser scanner. This technique may be assumed as benchmarking for accuracy assessment, but is more difficult to be operated in high-mountain areas. Nevertheless, the measurement of GCP’s for the terrestrial photogrammetric project has revealed to be a complex task, involving the need of a total station a GNSS. The effect of network geometry on the final output has also been investigated for SfM-Photogrammetry, considering the severe limitations implied in the Alpine environment.
DEM RECONSTRUCTION OF LARSEN B REGION BASED ON 1960S OPTICAL SATELLITE IMAGERY
Authors: Xia M. , Tang G. , Tian Y. , Ye W. , Li R. , Tong X.
Published: September 2017

Abstract. Antarctica is an important part of the earth system and crucial to global sea-level and climate change. In 1995, a number of US intelligence satellite photographs from the 1960s to 1970s have been declassified, only a portion of which (ARGON) covers the Antarctica. It provides a broader perspective to study the early Antarctica and it’s very important for us to study the changes in the early stage of Antarctica. In this paper, a hierarchical stereo image matching strategy was used to reconstruct a digital elevation model of the Larsen B region of Antarctica Peninsula in 1960s using ARGON images. The accuracy of the matching result estimated in all layers is within one scanned-pixel of 33 m and the accuracy of geometric modelling after bundle adjustment estimated by using check points is within one nominal pixel of 140 m. In the future, the elevation changes from 1960s of Larsen B tributary glaciers will be analysed.
A CURVATURE BASED ADAPTIVE NEIGHBORHOOD FOR INDIVIDUAL POINT CLOUD CLASSIFICATION
Authors: He E. , Chen Q. , Wang H. , Liu X.
Published: September 2017

Abstract. As a key step in 3D scene analysis, point cloud classification has gained a great deal of concerns in the past few years. Due to the uneven density, noise and data missing in point cloud, how to automatically classify the point cloud with a high precision is a very challenging task. The point cloud classification process typically includes the extraction of neighborhood based statistical information and machine learning algorithms. However, the robustness of neighborhood is limited to the density and curvature of the point cloud which lead to a label noise behavior in classification results. In this paper, we proposed a curvature based adaptive neighborhood for individual point cloud classification. Our main improvement is the curvature based adaptive neighborhood method, which could derive ideal 3D point local neighborhood and enhance the separability of features. The experiment result on Oakland benchmark dataset shows that the proposed method can effectively improve the classification accuracy of point cloud.
LAND COVERS CLASSIFICATION BASED ON RANDOM FOREST METHOD USING FEATURES FROM FULL-WAVEFORM LIDAR DATA
Authors: Ma L. , Zhou M. , Li C.
Published: September 2017

Abstract. In this study, a Random Forest (RF) based land covers classification method is presented to predict the types of land covers in Miyun area. The returned full-waveforms which were acquired by a LiteMapper 5600 airborne LiDAR system were processed, including waveform filtering, waveform decomposition and features extraction. The commonly used features that were distance, intensity, Full Width at Half Maximum (FWHM), skewness and kurtosis were extracted. These waveform features were used as attributes of training data for generating the RF prediction model. The RF prediction model was applied to predict the types of land covers in Miyun area as trees, buildings, farmland and ground. The classification results of these four types of land covers were obtained according to the ground truth information acquired from CCD image data of the same region. The RF classification results were compared with that of SVM method and show better results. The RF classification accuracy reached 89.73% and the classification Kappa was 0.8631.
CLUSTERING OF MULTISPECTRAL AIRBORNE LASER SCANNING DATA USING GAUSSIAN DECOMPOSITION
Authors: Morsy S. , Shaker A. , El-Rabbany A.
Published: September 2017

Abstract. With the evolution of the LiDAR technology, multispectral airborne laser scanning systems are currently available. The first operational multispectral airborne LiDAR sensor, the Optech Titan, acquires LiDAR point clouds at three different wavelengths (1.550, 1.064, 0.532 μm), allowing the acquisition of different spectral information of land surface. Consequently, the recent studies are devoted to use the radiometric information (i.e., intensity) of the LiDAR data along with the geometric information (e.g., height) for classification purposes. In this study, a data clustering method, based on Gaussian decomposition, is presented. First, a ground filtering mechanism is applied to separate non-ground from ground points. Then, three normalized difference vegetation indices (NDVIs) are computed for both non-ground and ground points, followed by histograms construction from each NDVI. The Gaussian function model is used to decompose the histograms into a number of Gaussian components. The maximum likelihood estimate of the Gaussian components is then optimized using Expectation – Maximization algorithm. The intersection points of the adjacent Gaussian components are subsequently used as threshold values, whereas different classes can be clustered. This method is used to classify the terrain of an urban area in Oshawa, Ontario, Canada, into four main classes, namely roofs, trees, asphalt and grass. It is shown that the proposed method has achieved an overall accuracy up to 95.1 % using different NDVIs.
A COMPARISON OF TREE SEGMENTATION METHODS USING VERY HIGH
DENSITY AIRBORNE LASER SCANNER DATA
Authors: Pirotti F. , Kobal M. , Roussel J. R.
Published: September 2017

Abstract. Developments of LiDAR technology are decreasing the unit cost per single point (e.g. single-photo counting). This brings to the possibility of future LiDAR datasets having very dense point clouds. In this work, we process a very dense point cloud (~200 points per square meter), using three different methods for segmenting single trees and extracting tree positions and other metrics of interest in forestry, such as tree height distribution and canopy area distribution. The three algorithms are tested at decreasing densities, up to a lowest density of ~5 point per square meter. Accuracy assessment is done using Kappa, recall, precision and F-Score metrics comparing results with tree positions from groundtruth measurements in six ground plots where tree positions and heights were surveyed manually. Results show that one method provides better Kappa and recall accuracy results for all cases, and that different point densities, in the range used in this study, do not affect accuracy significantly. Processing time is also considered; the method with better accuracy is several times slower than the other two methods and increases exponentially with point density. Best performer gave Kappa = 0.7. The implications of metrics for determining the accuracy of results of point positions’ detection is reported. Motives for the different performances of the three methods is discussed and further research direction is proposed.
MODELLING OF INDOOR ENVIRONMENTS USING LINDENMAYER SYSTEMS
Authors: Peter M.
Published: September 2017

Abstract. Documentation of the “as-built” state of building interiors has gained a lot of interest in the recent years. Various data acquisition methods exist, e.g. the extraction from photographed evacuation plans using image processing or, most prominently, indoor mobile laser scanning. Due to clutter or data gaps as well as errors during data acquisition and processing, automatic reconstruction of CAD/BIM-like models from these data sources is not a trivial task. Thus it is often tried to support reconstruction by general rules for the perpendicularity and parallelism which are predominant in man-made structures. Indoor environments of large, public buildings, however, often also follow higher-level rules like symmetry and repetition of e.g. room sizes and corridor widths. In the context of reconstruction of city city elements (e.g. street networks) or building elements (e.g. fac¸ade layouts), formal grammars have been put to use. In this paper, we describe the use of Lindenmayer systems - which originally have been developed for the computer-based modelling of plant growth - to model and reproduce the layout of indoor environments in 2D.
RESEARCH ON EVACUATION BASED ON SOCIAL FORCE MODEL
Authors: Liu W. , Deng Z. , Li W. , Lin J.
Published: September 2017

Abstract. Crowded centers always cause personnel casualties in evacuation operations. Stampede events often occur by hit, squeeze and crush due to panic. It is of vital important to alleviate such situation. With the deepening of personnel evacuation research, more and more researchers are committed to study individual behaviors and self-organization phenomenon in evacuation process. The study mainly includes: 1, enrich the social force model from different facets such as visual, psychological, external force to descript more realistic evacuation; 2, research on causes and effects of self – organization phenomenon. In this paper, we focus on disorder motion that occurs in the crowded indoor publics, especially the narrow channel and safety exits and other special arteries. We put forward the improved social force model to depict pedestrians’ behaviors, an orderly speed-stratification evacuation method to solve disorder problem, and shape-changed export to alleviate congestion. The result of this work shows an improvement of evacuation efficiency by 19.5 %. Guiding pedestrians’ direction to slow down the influence of social forces has a guidance function in improving the efficiency of indoor emergency evacuation.
REVEALING SPATIAL VARIATION AND CORRELATION OF URBAN TRAVELS FROM BIG TRAJECTORY DATA
Authors: Li X. , Tu W. , Shen S. , Yue Y. , Luo N. , Li Q.
Published: September 2017

Abstract. With the development of information and communication technology, spatial-temporal data that contain rich human mobility information are growing rapidly. However, the consistency of multi-mode human travel behind multi-source spatial-temporal data is not clear. To this aim, we utilized a week of taxies’ and buses’ GPS trajectory data and smart card data in Shenzhen, China to extract city-wide travel information of taxi, bus and metro and tested the correlation of multi-mode travel characteristics. Both the global correlation and local correlation of typical travel indicator were examined. The results show that: (1) Significant differences exist in of urban multi-mode travels. The correlation between bus travels and taxi travels, metro travel and taxi travels are globally low but locally high. (2) There are spatial differences of the correlation relationship between bus, metro and taxi travel. These findings help us understanding urban travels deeply therefore facilitate both the transport policy making and human-space interaction research.
INTEGRATED QUALITY EVALUATION OF THE IN-SITU NETWORKING MEASUREMENTS AND UPSCALING USING GAUSSIAN PROCESS REGRESSION
Authors: Yin G. , Li A.
Published: September 2017

Abstract. The flourished development of wireless sensor network technology sheds light to the effective and inexpensive collection of in-situ networking measurements. This will contribute to the temporal validation of coarse resolution remote sensing products. However, the quality evaluation of the in-situ networking measurements and upscaling is still problematic. This study proposed an evaluation method based on Gaussian Process Regression (GPR). Specifically, the qualities of networking measurements and upscaling were evaluated through the relevance of each plot, and the pixelwise coefficient of variation of the scaling results. Both of which can be generated by GPR. The preliminary results demonstrated the potential of the proposed method on quality evaluation of upscaling. Its potential on measurements (per se) quality evaluation will be analysed future.
AUTOMATIC CORRECTION ALGORITHM OF HYFROLOGY FEATURE ATTRIBUTE IN NATIONAL GEOGRAPHIC CENSUS
Authors: Li C. , Guo P. , Liu X.
Published: September 2017

Abstract. A subset of the attributes of hydrologic features data in national geographic census are not clear, the current solution to this problem was through manual filling which is inefficient and liable to mistakes. So this paper proposes an automatic correction algorithm of hydrologic features attribute. Based on the analysis of the structure characteristics and topological relation, we put forward three basic principles of correction which include network proximity, structure robustness and topology ductility. Based on the WJ-III map workstation, we realize the automatic correction of hydrologic features. Finally, practical data is used to validate the method. The results show that our method is highly reasonable and efficient.
RESEARCH ON AIRBORNE SAR IMAGING BASED ON ESC ALGORITHM
Authors: Dong X. T. , Yue X. J. , Zhao Y. H. , Han C. M.
Published: September 2017

Abstract. Due to the ability of flexible, accurate, and fast obtaining abundant information, airborne SAR is significant in the field of Earth Observation and many other applications. Optimally the flight paths are straight lines, but in reality it is not the case since some portion of deviation from the ideal path is impossible to avoid. A small disturbance from the ideal line will have a major effect on the signal phase, dramatically deteriorating the quality of SAR images and data. Therefore, to get accurate echo information and radar images, it is essential to measure and compensate for nonlinear motion of antenna trajectories. By means of compensating each flying trajectory to its reference track, MOCO method corrects linear phase error and quadratic phase error caused by nonlinear antenna trajectories. Position and Orientation System (POS) data is applied to acquiring accuracy motion attitudes and spatial positions of antenna phase centre (APC). In this paper, extend chirp scaling algorithm (ECS) is used to deal with echo data of airborne SAR. An experiment is done using VV-Polarization raw data of C-band airborne SAR. The quality evaluations of compensated SAR images and uncompensated SAR images are done in the experiment. The former always performs better than the latter. After MOCO processing, azimuth ambiguity is declined, peak side lobe ratio (PSLR) effectively improves and the resolution of images is improved obviously. The result shows the validity and operability of the imaging process for airborne SAR.
STUDY ON LAND SUBSIDENCE INCANGZHOU AREA BASEDON SENTINEL-1A/B
DATA
Authors: Zhou H. , Wang Y. , Yan S.
Published: September 2017

Abstract. This paper, obtaining 39scenesof images of the Sentinel-1 A/B, monitored the Cangzhou area subsidence from Mar. 2015 to Dec. 2016 basing on using PS-InSAR technique. The annual average subsidence rate and accumulative subsidence were obtained. The results showed that the ground surface of Xian County,Cang County, Cangzhou urban area had a rebound trend; Qing County, the east of Cang County ,the west of Nanpi County and Dongguang County appeared obvious subsidence, and the accumulated subsidence in Hezhuang village of Dongguang County reached 47 mm. And from that the main reason leading to these obvious subsidence was over-exploitation of ground-water. At last, it analyzed the settlement of the High-Speed Railway (HR) which was north from the Machang town of QingCounty and south to the Lian town of Dongguang County in Cangzhou.The relative deformation of the HR between the two sections which was Lierzhuang village of Cang County and Chenxin village of Nanpi County arrived at 30 mm. Moreover, this paper discussed the application of Sentinel-1 A/B SAR images in monitoring urban land subsidence and the results provided important basic data for the relevant departments.
A ROBUST DESCRIPTOR BASED ON SPATIAL AND FREQUENCY STRUCTURAL INFORMATION FOR VISIBLE AND THERMAL INFRARED IMAGE MATCHING
Authors: Fu Z. , Qin Q. , Wu C. , Chang Y. , Luo B.
Published: September 2017

Abstract. Due to the differences of imaging principles, image matching between visible and thermal infrared images still exist new challenges and difficulties. Inspired by the complementary spatial and frequency information of geometric structural features, a robust descriptor is proposed for visible and thermal infrared images matching. We first divide two different spatial regions to the region around point of interest, using the histogram of oriented magnitudes, which corresponds to the 2-D structural shape information to describe the larger region and the edge oriented histogram to describe the spatial distribution for the smaller region. Then the two vectors are normalized and combined to a higher feature vector. Finally, our proposed descriptor is obtained by applying principal component analysis (PCA) to reduce the dimension of the combined high feature vector to make our descriptor more robust. Experimental results showed that our proposed method was provided with significant improvements in correct matching numbers and obvious advantages by complementing information within spatial and frequency structural information.
A NEW APPROACH FOR PROGRESSIVE DENSE RECONSTRUCTION FROM CONSECUTIVE IMAGES BASED ON PRIOR LOW-DENSITY 3D POINT CLOUDS
Authors: Lari Z. , El-Sheimy N.
Published: September 2017

Abstract. In recent years, the increasing incidence of climate-related disasters has tremendously affected our environment. In order to effectively manage and reduce dramatic impacts of such events, the development of timely disaster management plans is essential. Since these disasters are spatial phenomena, timely provision of geospatial information is crucial for effective development of response and management plans. Due to inaccessibility of the affected areas and limited budget of first-responders, timely acquisition of the required geospatial data for these applications is usually possible only using low-cost imaging and georefencing sensors mounted on unmanned platforms. Despite rapid collection of the required data using these systems, available processing techniques are not yet capable of delivering geospatial information to responders and decision makers in a timely manner. To address this issue, this paper introduces a new technique for dense 3D reconstruction of the affected scenes which can deliver and improve the needed geospatial information incrementally. This approach is implemented based on prior 3D knowledge of the scene and employs computationally-efficient 2D triangulation, feature descriptor, feature matching and point verification techniques to optimize and speed up 3D dense scene reconstruction procedure. To verify the feasibility and computational efficiency of the proposed approach, an experiment using a set of consecutive images collected onboard a UAV platform and prior low-density airborne laser scanning over the same area is conducted and step by step results are provided. A comparative analysis of the proposed approach and an available image-based dense reconstruction technique is also conducted to prove the computational efficiency and competency of this technique for delivering geospatial information with pre-specified accuracy.
COMPRESSIVE SENSING IMAGE FUSION BASED ON PARTICLE SWARM OPTIMIZATION ALGORITHM
Authors: Li X. , Lv J. , Jiang S. , Zhou H.
Published: September 2017

Abstract. In order to solve the problem that the spatial matching is difficult and the spectral distortion is large in traditional pixel-level image fusion algorithm. We propose a new method of image fusion that utilizes HIS transformation and the recently developed theory of compressive sensing that is called HIS-CS image fusion. In this algorithm, the particle swarm optimization algorithm is used to select the fusion coefficient ω. In the iterative process, the image fusion coefficient ω is taken as particle, and the optimal value is obtained by combining the optimal objective function. Then we use the compression-aware weighted fusion algorithm for remote sensing image fusion, taking the coefficient ω as the weight value. The algorithm ensures the optimal selection of fusion effect with a certain degree of self-adaptability. To evaluate the fused images, this paper uses five kinds of index parameters such as Entropy, Standard Deviation, Average Gradient, Degree of Distortion and Peak Signal-to-Noise Ratio. The experimental results show that the image fusion effect of the algorithm in this paper is better than that of traditional methods.
A FRAMEWORK OF CHANGE DETECTION BASED ON COMBINED MORPHOLOGICA FEATURES AND MULTI-INDEX CLASSIFICATION
Authors: Li S. , Zhang S. , Yang D.
Published: September 2017

Abstract. Remote sensing images are particularly well suited for analysis of land cover change. In this paper, we present a new framework for detection of changing land cover using satellite imagery. Morphological features and a multi-index are used to extract typical objects from the imagery, including vegetation, water, bare land, buildings, and roads. Our method, based on connected domains, is different from traditional methods; it uses image segmentation to extract morphological features, while the enhanced vegetation index (EVI), the differential water index (NDWI) are used to extract vegetation and water, and a fragmentation index is used to the correct extraction results of water. HSV transformation and threshold segmentation extract and remove the effects of shadows on extraction results. Change detection is performed on these results. One of the advantages of the proposed framework is that semantic information is extracted automatically using low-level morphological features and indexes. Another advantage is that the proposed method detects specific types of change without any training samples. A test on ZY-3 images demonstrates that our framework has a promising capability to detect change.
THE INFLUENCE OF SPATIAL FAMILIARITY ON THE LANDMARK SALIENCE SENSIBILITY IN PEDESTRIAN NAVIGATION ENVIRONMENT
Authors: Li X , Wu X.-Q. , Yin Z.-H. , Shen J.
Published: September 2017

Abstract. To contribute to a more effective design of landmark navigation guidance, this paper is concerned with the relationship between the spatial familiarity and landmark salience, which includes visual, semantic and structural attributes. The link of those two is the subjective judgment of users, which is called landmark salience sensibility. In order to explore the influence of spatial familiarity on the landmark salience sensibility, we selected two types of experimental area including campus and commercial district and four groups of experimental subject with different spatial familiarity degree. After the whole walking process, subjects are asked to draw a navigation sketch for themselves. Depending on the landmarks remaining in the sketch, we calculated the three attributes of the mean landmark salience to represent the landmark salience sensibility of each group for both paths. The result shows that with the increase of spatial familiarity, the landmark salience sensitivity is gradually reduced and the ascending order of attention degree to the attributes of the landmark salience is visual, semantic and structural salience. This conclusion is supportive to the study of landmark extraction and pedestrian guidance. Because the outdoor landmarks are analysed, we propose that in the future indoor landmarks are needed to be concerned.
THE ROLE OF VENEZUELAN SPACE TECHNOLOGY IN PROMOTING
DEVELOPMENT IN LATIN AMERICA
Authors: Pena J. A. , Yumin T.
Published: September 2017

Abstract. Space technology and resources are used around the world to address societal challenges. Space provides valuable satellite services, unique scientific discoveries, surprising technology applications and new economic opportunities. Venezuela formally recognizes the advantages of space resources and pursues national level activity to harness them. Venezuela space cooperation has grown in the past several years, contributing to debates over Venezuela’s rising influence in the Latin America. This paper summarizes the establishment and current development of space activities in the Bolivarian Republic of Venezuela, these activities are focused on the areas of telecommunications, Earth observation, research and development space and has as a primary goal the satisfaction of social needs. This analysis offers the elements most important of the Venezuelan space policy: technological transfer, capacity building and human training and international cooperation including the new participation of Venezuela in the international charter on space and major disasters. Our analysis shows that Venezuela has the potential to become a space leadership country, promoting the social welfare, integration, and sustainable development of Latin American countries.
INTEGRATION OF RS/GIS FOR SURFACE WATER POLLUTION RISK MODELING. CASE STUDY: AL-ABRASH SYRIAN COASTAL BASIN
Authors: Yaghi Y. , Salim H.
Published: September 2017

Abstract. Recently the topic of the quality of surface water (rivers – lakes) and the sea is an important topics at different levels. It is known that there are two major groups of pollutants: Point Source Pollution (PSP) and non-point Source pollution (NPSP). Historically most of the surface water pollution protection programs dealing with the first set of pollutants which comes from sewage pipes and factories drainage. With the growing need for current and future water security must stand on the current reality of the coastal rivers basin in terms of freshness and cleanliness and condition of water pollution. This research aims to assign the NPS pollutants that reach Al Abrash River and preparation of databases and producing of risk Pollution map for NPS pollutants in order to put the basin management plan to ensure the reduction of pollutants that reach the river. This research resulted of establishing of Databases of NPSP (Like pesticides and fertilizers) and producing of thematic maps for pollution severity and pollution risk based on the pollution models designed in GIS environment and utilizing from remote sensing data. Preliminary recommendations for managing these pollutants were put.
A STUDY OF UNSUPERVISED CHANGE DETECTION BASED ON TEST STATISTIC
AND GAUSSIAN MIXTURE MODEL USING POLSAR SAR DATA
Authors: Yang Y. , Liu W.
Published: September 2017

Abstract. To solve the problems of existing method of change detection using fully polarimetric SAR which not takes full advantage of polarimetric information and the result of false alarm rate of which is high, a method is proposed based on test statistic and Gaussian mixture model in this paper. In the case of the flood disaster in Wuhan city in 2016, difference image is obtained by the likelihoodratio parameter which is built using coherency matrix C3 or covariance matrix T3 of fully polarimetric SAR based on test statistic, and it becomes a reality that the change information is automatic extracted by the parameter of Gaussian mixture model (GMM) of difference image based on the expectation maximization (EM) iterative algorithm. The experimental results show that the overall accuracy of change detection results can be improved and false alarm rate can be reduced using this method by comparison with traditional constant false alarm rate (CFAR) method. Thus the validity and feasibility of the method is demonstrated.
A METHOD OF EXTRACTING SHORELINE BASED ON SEMANTIC INFORMATION USING DUAL-LENGTH LiDAR DATA
Authors: Yao C. , Wu J. , Yuan Z.
Published: September 2017

Abstract. Shoreline is a spatial varying separation between water and land. By utilizing dual-wavelength LiDAR point data together with semantic information that shoreline often appears beyond water surface profile and is observable on the beach, the paper generates the shoreline and the details are as follows: (1) Gain the water surface profile: first we obtain water surface by roughly selecting water points based on several features of water body, then apply least square fitting method to get the whole water trend surface. Then we get the ground surface connecting the under -water surface by both TIN progressive filtering method and surface interpolation method. After that, we have two fitting surfaces intersected to get water surface profile of the island. (2) Gain the sandy beach: we grid all points and select the water surface profile grids points as seeds, then extract sandy beach points based on eight-neighborhood method and features, then we get all sandy beaches. (3) Get the island shoreline: first we get the sandy beach shoreline based on intensity information, then we get a threshold value to distinguish wet area and dry area, therefore we get the shoreline of several sandy beaches. In some extent, the shoreline has the same height values within a small area, by using all the sandy shoreline points to fit a plane P, and the intersection line of the ground surface and the shoreline plane P can be regarded as the island shoreline. By comparing with the surveying shoreline, the results show that the proposed method can successfully extract shoreline.