bluuebunny/crossref_metadata_2025 huggingface dataset:
POSE ESTIMATION OF UNMANNED AERIAL VEHICLES BASED ON A VISION-AIDED MULTI-SENSOR FUSION
Authors: Abdi G. , Samadzadegan F. , Kurz F.
Published: August 2016

GNSS/IMU navigation systems offer low-cost and robust solution to navigate UAVs. Since redundant measurements greatly improve the reliability of navigation systems, extensive researches have been made to enhance the efficiency and robustness of GNSS/IMU by additional sensors. This paper presents a method for integrating reference data, images taken from UAVs, barometric height data and GNSS/IMU data to estimate accurate and reliable pose parameters of UAVs. We provide improved pose estimations by integrating multi-sensor observations in an EKF algorithm with IMU motion model. The implemented methodology has demonstrated to be very efficient and reliable for automatic pose estimation. The calculated position and attitude of the UAV especially when we removed the GNSS from the working cycle clearly indicate the ability of the purposed methodology.
THE CLASSICAL ASSUMPTION TEST TO DRIVING FACTORS OF LAND COVER CHANGE IN THE DEVELOPMENT REGION OF NORTHERN PART OF WEST JAVA
Authors: Ainiyah Nur , Deliar Albertus , Virtriana Riantini
Published: August 2016

Land cover changes continuously change by the time. Many kind of phenomena is a simple of important factors that affect the environment change, both locally and also globally. To determine the existence of the phenomenon of land cover change in a region, it is necessary to identify the driving factors that can cause land cover change. The relation between driving factors and response variables can be evaluated by using regression analysis techniques. In this case, land cover change is a dichotomous phenomenon (binary). The BLR’s model (Binary Logistic Regression) is the one of kind regression analysis which can be used to describe the nature of dichotomy. Before performing regression analysis, correlation analysis is carried it the first. Both correlation test and regression tests are part of a statistical test or known classical assumption test. From result of classical assumption test, then can be seen that the data used to perform analysis from driving factors of the land cover changes is proper with used by BLR’s method. Therefore, the objective of this research is to evaluate the effectiveness of methods in assessing the relation between driving factors of land cover change that assumed can affect to land cover change phenomena. This research will use the classical assumed test of multiple regression linear analysis, showing that BLR method is efficiency and effectiveness solution for researching or studying in phenomenon of land cover changes. So it will to provide certainty that the regression equation obtained has accuracy in estimation, unbiased and consistent.
LOST IN THE CLOUD - NEW CHALLENGES FOR TEACHING GIS
Authors: Bellman C. J. , Pupedis G.
Published: August 2016

As cloud based services move towards becoming the dominant paradigm in many areas of information technology, GIS has also moved into ‘the Cloud’, creating a new opportunities for professionals and students alike, while at the same time presenting a range of new challenges and opportunities for GIS educators. Learning for many students in the geospatial science disciplines has been based on desktop software for GIS, building their skills from basic data handling and manipulation to advanced spatial analysis and database storage. Cloud-based systems challenge this paradigm in many ways, with some of the skills being replaced by clever and capable software tools, while the ubiquitous nature of the computing environment offers access and processing from anywhere, on any device.

 This paper describes our experiences over the past two years in developing and delivering a new course incorporating cloud based technologies for GIS and illustrates the many benefits and pitfalls of a cloud based approach to teaching. Throughout the course, students were encouraged to provide regular feedback on the course through the use of online journals. This allowed students to critique the approach to teaching, the learning materials available and to describe their own level of comfort and engagement with the material in an honest and non-confrontational manner. Many of the students did not have a strong information technology background and the journals provided great insight into the views of the students and the challenges they faced in mastering this technology.
MEASURING POLYCENTRICITY OF MEGA-CITY REGIONS IN CHINA BASED ON THE INTERCITY MIGRATION FLOWS
Authors: Mu Xiaoyan , Yeh Anthony G. O.
Published: August 2016

This paper uses the intercity migration flows to examine relations between Chinese cities, identify the important mega-city regions and measure each region's polycentricity from an interaction perspective. Data set contains the long-term residential migration trajectories of three million Sina weibo users across 345 cities. Cities with close connectivity deployed around one or several mega cities are identified as mega-city regions. Features of the mega-city regions are characterized by the strength of migration flows, density of connections, and regional migration patterns. The results show that the disparities exist in different mega-city regions; most mega-city regions are lack of polycentricity.
E-LEARNING IN PHOTOGRAMMETRY, REMOTE SENSING AND SPATIAL INFORMATION SCIENCE
Authors: Vyas Anjana , König Gerhard
Published: August 2016

Science and technology are evolving leaps and bounds. The advancements in GI-Science for natural and built environment helps in improving the quality of life. Learning through education and training needs to be at par with those advancements, which plays a vital role in utilization of technology. New technologies that creates new opportunities have enabled Geomatics to broaden the horizon (skills and competencies). Government policies and decisions support the use of geospatial science in various sectors of governance. Mapping, Land management, Urban planning, Environmental planning, Industrialization are some of the areas where the geomatics has become a baseline for decision making at national level. There is a need to bridge the gap between developments in geospatial science and its utilization and implementation. To prepare a framework for standardisation it is important to understand the theories of education and prevailing practices, with articulate goals exploring variety of teaching techniques.

 E-Learning is an erudition practice shaped for facilitating learning and improving performance by creating, using and managing appropriate technological processes and resources through digital and network-enabled technology. It is a shift from traditional education or training to ICT-based flexible and collaborative learning based on the community of learners, academia, professionals, experts and facilitators. Developments in e-learning is focussed on computer assisted learning which has become popular because of its potential for providing more flexible access to content and instruction at any time, from any place (Means et al, 2009). With the advent of the geo-spatial technology, fast development in the software and hardware, the demand for skilled manpower is increasing and the need is for training, education, research and dissemination. It suggests inter-organisational cooperation between academia, industry, government and international collaboration. There is a nascent need to adopt multi-specialisation approach to examine the issues and challenges of research in such a valued topic of education and training in multi-disciplinary areas. Learning involve a change in an individual's knowledge, ability to perform a skill, participate and communicate. There is considerable variation among the theories about the nature of this change. This paper derives from a scientific research grant received from ISPRS, reveals a summary result from assessing various theories and methods of evaluation of learning through education, system and structure of it for GeoInformatics.
LATEST DEVELOPMENTS OF THE ISPRS STUDENT CONSORTIUM
Authors: Detchev I. , Kanjir U. , Reyes S. R. , Miyazaki H. , Aktas A. F.
Published: August 2016

The International Society for Photogrammetry and Remote Sensing (ISPRS) Student Consortium (SC) is a network for young professionals studying or working within the fields of photogrammetry, remote sensing, Geographical Information Systems (GIS), and other related geo-spatial sciences. The main goal of the network is to provide means for information exchange for its young members and thus help promote and integrate youth into the ISPRS. Over the past four years the Student Consortium has successfully continued to fulfil its mission in both formal and informal ways. The formal means of communication of the SC are its website, newsletter, e-mail announcements and summer schools, while its informal ones are multiple social media outlets and various social activities during student related events. The newsletter is published every three to four months and provides both technical and experiential content relevant for the young people in the ISPRS. The SC has been in charge or at least has helped with organizing one or more summer schools every year. The organization's e-mail list has over 1,100 subscribers, its website hosts over 1,300 members from 100 countries across the entire globe, and its public Facebook group currently has over 4,500 joined visitors, who connect among one another and share information relevant for their professional careers. These numbers show that the Student Consortium has grown into a significant online-united community. The paper will present the organization’s on-going and past activities for the last four years, its current priorities and a strategic plan and aspirations for the future four-year period.
THE USE OF UAS FOR RAPID 3D MAPPING IN GEOMATICS EDUCATION
Authors: Teo Tee-Ann , Shih Peter Tian-Yuan , Yu Sz-Cheng , Tsai Fuan
Published: August 2016

With the development of technology, UAS is an advance technology to support rapid mapping for disaster response. The aim of this study is to develop educational modules for UAS data processing in rapid 3D mapping. The designed modules for this study are focused on UAV data processing from available freeware or trial software for education purpose. The key modules include orientation modelling, 3D point clouds generation, image georeferencing and visualization. The orientation modelling modules adopts VisualSFM to determine the projection matrix for each image station. Besides, the approximate ground control points are measured from OpenStreetMap for absolute orientation. The second module uses SURE and the orientation files from previous module for 3D point clouds generation. Then, the ground point selection and digital terrain model generation can be archived by LAStools. The third module stitches individual rectified images into a mosaic image using Microsoft ICE (Image Composite Editor). The last module visualizes and measures the generated dense point clouds in CloudCompare. These comprehensive UAS processing modules allow the students to gain the skills to process and deliver UAS photogrammetric products in rapid 3D mapping. Moreover, they can also apply the photogrammetric products for analysis in practice.
IMAGE QUATY ASSESSMENT FOR VHR REMOTE SENSING IMAGE CLASSIFICATION
Authors: Li Zhipeng , Shen Li , Wu Linmei
Published: August 2016

The data from remote sensing images are widely used for characterizing land use and land cover at present. With the increasing availability of very high resolution (VHR) remote sensing images, the remote sensing image classification becomes more and more important for information extraction. The VHR remote sensing images are rich in details, but high within-class variance as well as low between-class variance make the classification of ground cover a difficult task. What’s more, some related studies show that the quality of VHR remote sensing images also has a great influence on the ability of the automatic image classification. Therefore, the research that how to select the appropriate VHR remote sensing images to meet the application of classification is of great significance. In this context, the factors of VHR remote sensing image classification ability are discussed and some indices are selected for describing the image quality and the image classification ability objectively. Then, we explore the relationship of the indices of image quality and image classification ability under a specific classification framework. The results of the experiments show that these image quality indices are not effective for indicating the image classification ability directly. However, according to the image quality metrics, we can still propose some suggestion for the application of classification.
A SIMPLE INTERPRETATION OF THE RICE SPECTRAL INDICES SPACE FOR ASSESSMENT OF HEAVY METAL STRESS
Authors: Wang P. , Huang F. , Liu X. N.
Published: August 2016

Heavy metal stress will induce the change of the bio-parameters like chlorophyll, nitrogen and water content of rice. In this paper, we analyzed the traditional spectral index which has strong relationship in general with the three bio-parameters using hyperspectral data acquired by ASD. It is found that some indies do not work well when the heavy metal stress exists, however, some indies still has ability to estimate the above three bio-parameters. A new interpretation is proposed to classify the stress level based on both the physical mechanism analysis and the statistic model after we describe and discuss studies on the expression of spectral indices of rice under heavy metal stress. The 3-axes spectral indices spaces, which are constructed of 3 spectral indices sensitive to rice’s chlorophyll concentration, nitrogen concentration and water concentration respectively, are used to visualize the linkage between heavy metal stress and spectrum of rice canopy.
AUTOMATIC GENERATION OF BUILDING MAPPING USING DIGITAL, VERTICAL AND AERIAL HIGH RESOLUTION PHOTOGRAPHS AND LIDAR POINT CLOUDS
Authors: Barragán W. , Campos A. , Sanchez G.
Published: August 2016

The objective of this research is automatic generation of buildings in the interest areas. This research was developed by using high resolution vertical aerial photographs and the LIDAR point cloud through radiometric and geometric digital processes. The research methodology usesknown building heights and various segmentation algorithms and spectral band combination. The overall effectiveness of the algorithm is 97.2% with the test data.
A METHOD TO ESTIMATE TEMPORAL INTERACTION IN A CONDITIONAL RANDOM FIELD BASED APPROACH FOR CROP RECOGNITION
Authors: Diaz P. M. A. , Feitosa R. Q. , Sanches I. D. , Costa G. A. O. P.
Published: August 2016

This paper presents a method to estimate the temporal interaction in a Conditional Random Field (CRF) based approach for crop recognition from multitemporal remote sensing image sequences. This approach models the phenology of different crop types as a CRF. Interaction potentials are assumed to depend only on the class labels of an image site at two consecutive epochs. In the proposed method, the estimation of temporal interaction parameters is considered as an optimization problem, whose goal is to find the transition matrix that maximizes the CRF performance, upon a set of labelled data. The objective functions underlying the optimization procedure can be formulated in terms of different accuracy metrics, such as overall and average class accuracy per crop or phenological stages. To validate the proposed approach, experiments were carried out upon a dataset consisting of 12 co-registered LANDSAT images of a region in southeast of Brazil. Pattern Search was used as the optimization algorithm. The experimental results demonstrated that the proposed method was able to substantially outperform estimates related to joint or conditional class transition probabilities, which rely on training samples.
PARALLEL IMPLEMENTATION OF MORPHOLOGICAL PROFILE BASED SPECTRAL-SPATIAL CLASSIFICATION SCHEME FOR HYPERSPECTRAL IMAGERY
Authors: Kumar B. , Dikshit O.
Published: August 2016

Extended morphological profile (EMP) is a good technique for extracting spectral-spatial information from the images but large size of hyperspectral images is an important concern for creating EMPs. However, with the availability of modern multi-core processors and commodity parallel processing systems like graphics processing units (GPUs) at desktop level, parallel computing provides a viable option to significantly accelerate execution of such computations. In this paper, parallel implementation of an EMP based spectralspatial classification method for hyperspectral imagery is presented. The parallel implementation is done both on multi-core CPU and GPU. The impact of parallelization on speed up and classification accuracy is analyzed. For GPU, the implementation is done in compute unified device architecture (CUDA) C. The experiments are carried out on two well-known hyperspectral images. It is observed from the experimental results that GPU implementation provides a speed up of about 7 times, while parallel implementation on multi-core CPU resulted in speed up of about 3 times. It is also observed that parallel implementation has no adverse impact on the classification accuracy.
MULTIPLE REFLECTION EFFECTS IN NONLINEAR MIXTURE MODEL FOR HYPERSPECTRAL IMAGE ANALYSIS
Authors: Liu C. Y. , Ren H.
Published: August 2016

Hyperspectral spectrometers can record electromagnetic energy with hundreds or thousands of spectral channels. With such high spectral resolution, the spectral information has better capability for material identification. Because of the spatial resolution, one pixel in hyperspectral images usually covers several meters, and it may contain more than one material. Therefore, the mixture model must be considered. Linear mixture model (LMM) has been widely used for remote sensing target classifications, because of its simplicity and yields reasonable results for smooth surfaces. For rough surfaces, the physical interactions of the light scattered between multiple materials in the scene must be considered. Recently, Generalized Bilinear Model (GBM) is proposed and it includes the double reflection between different materials into a nonlinear model, but it ignores the interactions within the same material. In this study, we propose a modified version of GBM to further consider this effect in our model, called Modified Generalized Bilinear Model (MGBM).
SOIL SALINITY MAPPING USING MULTITEMPORAL LANDSAT DATA
Authors: Azabdaftari A. , Sunar F.
Published: August 2016

Soil salinity is one of the most important problems affecting many areas of the world. Saline soils present in agricultural areas reduce the annual yields of most crops. This research deals with the soil salinity mapping of Seyhan plate of Adana district in Turkey from the years 2009 to 2010, using remote sensing technology. In the analysis, multitemporal data acquired from LANDSAT 7-ETM+ satellite in four different dates (19 April 2009, 12 October 2009, 21 March 2010, 31 October 2010) are used. As a first step, preprocessing of Landsat images is applied. Several salinity indices such as NDSI (Normalized Difference Salinity Index), BI (Brightness Index) and SI (Salinity Index) are used besides some vegetation indices such as NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index) and EVI (Enhamced Vegetation Index) for the soil salinity mapping of the study area. The field’s electrical conductivity (EC) measurements done in 2009 and 2010, are used as a ground truth data for the correlation analysis with the original band values and different index image bands values. In the correlation analysis, two regression models, the simple linear regression (SLR) and multiple linear regression (MLR) are considered. According to the highest correlation obtained, the 21st March, 2010 dataset is chosen for production of the soil salinity map in the area. Finally, the efficiency of the remote sensing technology in the soil salinity mapping is outlined.
UNSUPERVISED WISHART CLASSFICATION OF WETLANDS IN NEWFOUNDLAND, CANADA USING POLSAR DATA BASED ON FISHER LINEAR DISCRIMINANT ANALYSIS
Authors: Mohammadimanesh F. , Salehi B. , Mahdianpari M. , Homayouni S.
Published: August 2016

Polarimetric Synthetic Aperture Radar (PolSAR) imagery is a complex multi-dimensional dataset, which is an important source of information for various natural resources and environmental classification and monitoring applications. PolSAR imagery produces valuable information by observing scattering mechanisms from different natural and man-made objects. Land cover mapping using PolSAR data classification is one of the most important applications of SAR remote sensing earth observations, which have gained increasing attention in the recent years. However, one of the most challenging aspects of classification is selecting features with maximum discrimination capability. To address this challenge, a statistical approach based on the Fisher Linear Discriminant Analysis (FLDA) and the incorporation of physical interpretation of PolSAR data into classification is proposed in this paper. After pre-processing of PolSAR data, including the speckle reduction, the H/α classification is used in order to classify the basic scattering mechanisms. Then, a new method for feature weighting, based on the fusion of FLDA and physical interpretation, is implemented. This method proves to increase the classification accuracy as well as increasing between-class discrimination in the final Wishart classification. The proposed method was applied to a full polarimetric C-band RADARSAT-2 data set from Avalon area, Newfoundland and Labrador, Canada. This imagery has been acquired in June 2015, and covers various types of wetlands including bogs, fens, marshes and shallow water. The results were compared with the standard Wishart classification, and an improvement of about 20% was achieved in the overall accuracy. This method provides an opportunity for operational wetland classification in northern latitude with high accuracy using only SAR polarimetric data.
A METHOD FOR THE EXTRACTION OF LONG-TERM DEFORMATION CHARACTERISTICS OF LONG-SPAN HIGH-SPEED RAILWAY BRIDGES USING HIGH-RESOLUTION SAR IMAGES
Authors: Jia H. G. , Liu L. Y.
Published: August 2016

Natural causes and high-speed train load will result in the structural deformation of long-span bridges, which greatly influence the safety operation of high-speed railway. Hence it is necessary to conduct the deformation monitoring and regular status assessment for long-span bridges. However for some traditional surveying technique, e.g. control-point-based surveying techniques, a lot of human and material resources are needed to perform the long-term monitoring for the whole bridge. In this study we detected the long-term bridge deformation time-series by persistent scatterer interferometric synthetic aperture radar (PSInSAR) technique using the high-resolution SAR images and external digital elevation model. A test area in Nanjing city in China is chosen and TerraSAR-X images and Tandem-X for this area have been used. There is the Dashengguan bridge in high speed railway in this area as study object to evaluate this method. Experiment results indicate that the proposed method can effectively extract the long-term deformation of long-span high-speed railway bridge with higher accuracy.
BALANCED VS IMBALANCED TRAINING DATA: CLASSIFYING RAPIDEYE DATA WITH SUPPORT VECTOR MACHINES
Authors: Ustuner M. , Sanli F. B. , Abdikan S.
Published: August 2016

The accuracy of supervised image classification is highly dependent upon several factors such as the design of training set (sample selection, composition, purity and size), resolution of input imagery and landscape heterogeneity. The design of training set is still a challenging issue since the sensitivity of classifier algorithm at learning stage is different for the same dataset. In this paper, the classification of RapidEye imagery with balanced and imbalanced training data for mapping the crop types was addressed. Classification with imbalanced training data may result in low accuracy in some scenarios. Support Vector Machines (SVM), Maximum Likelihood (ML) and Artificial Neural Network (ANN) classifications were implemented here to classify the data. For evaluating the influence of the balanced and imbalanced training data on image classification algorithms, three different training datasets were created. Two different balanced datasets which have 70 and 100 pixels for each class of interest and one imbalanced dataset in which each class has different number of pixels were used in classification stage. Results demonstrate that ML and NN classifications are affected by imbalanced training data in resulting a reduction in accuracy (from 90.94% to 85.94% for ML and from 91.56% to 88.44% for NN) while SVM is not affected significantly (from 94.38% to 94.69%) and slightly improved. Our results highlighted that SVM is proven to be a very robust, consistent and effective classifier as it can perform very well under balanced and imbalanced training data situations. Furthermore, the training stage should be precisely and carefully designed for the need of adopted classifier.
COUPLING REGULAR TESSELLATION WITH RJMCMC ALGORITHM TO SEGMENT SAR IMAGE WITH UNKNOWN NUMBER OF CLASSES
Authors: Wang Y. , Li Y. , Zhao Q. H.
Published: August 2016

This paper presents a Synthetic Aperture Radar (SAR) image segmentation approach with unknown number of classes, which is based on regular tessellation and Reversible Jump Markov Chain Monte Carlo (RJMCMC') algorithm. First of all, an image domain is portioned into a set of blocks by regular tessellation. The image is modeled on the assumption that intensities of its pixels in each homogeneous region satisfy an identical and independent Gamma distribution. By Bayesian paradigm, the posterior distribution is obtained to build the region-based image segmentation model. Then, a RJMCMC algorithm is designed to simulate from the segmentation model to determine the number of homogeneous regions and segment the image. In order to further improve the segmentation accuracy, a refined operation is performed. To illustrate the feasibility and effectiveness of the proposed approach, two real SAR image is tested.
A KERNEL METHOD BASED ON TOPIC MODEL FOR VERY HIGH SPATIAL RESOLUTION (VHSR) REMOTE SENSING IMAGE CLASSIFICATION
Authors: Wu Linmei , Shen Li , Li Zhipeng
Published: August 2016

A kernel-based method for very high spatial resolution remote sensing image classification is proposed in this article. The new kernel method is based on spectral-spatial information and structure information as well, which is acquired from topic model, Latent Dirichlet Allocation model. The final kernel function is defined as K = u1Kspec + u2Kspat + u3Kstru, in which Kspec, Kspat, Kstru are radial basis function (RBF) and u1 + u2 + u3 = 1. In the experiment, comparison with three other kernel methods, including the spectral-based, the spectral- and spatial-based and the spectral- and structure-based method, is provided for a panchromatic QuickBird image of a suburban area with a size of 900 × 900 pixels and spatial resolution of 0.6 m. The result shows that the overall accuracy of the spectral- and structure-based kernel method is 80 %, which is higher than the spectral-based kernel method, as well as the spectral- and spatial-based which accuracy respectively is 67 % and 74 %. What's more, the accuracy of the proposed composite kernel method that jointly uses the spectral, spatial, and structure information is highest among the four methods which is increased to 83 %. On the other hand, the result of the experiment also verifies the validity of the expression of structure information about the remote sensing image.
CLASSIFICATION OF URBAN AERIAL DATA BASED ON PIXEL LABELLING WITH DEEP CONVOLUTIONAL NEURAL NETWORKS AND LOGISTIC REGRESSION
Authors: Yao W. , Poleswki P. , Krzystek P.
Published: August 2016

The recent success of deep convolutional neural networks (CNN) on a large number of applications can be attributed to large amounts of available training data and increasing computing power. In this paper, a semantic pixel labelling scheme for urban areas using multi-resolution CNN and hand-crafted spatial-spectral features of airborne remotely sensed data is presented. Both CNN and hand-crafted features are applied to image/DSM patches to produce per-pixel class probabilities with a L1-norm regularized logistical regression classifier. The evidence theory infers a degree of belief for pixel labelling from different sources to smooth regions by handling the conflicts present in the both classifiers while reducing the uncertainty. The aerial data used in this study were provided by ISPRS as benchmark datasets for 2D semantic labelling tasks in urban areas, which consists of two data sources from LiDAR and color infrared camera. The test sites are parts of a city in Germany which is assumed to consist of typical object classes including impervious surfaces, trees, buildings, low vegetation, vehicles and clutter. The evaluation is based on the computation of pixel-based confusion matrices by random sampling. The performance of the strategy with respect to scene characteristics and method combination strategies is analyzed and discussed. The competitive classification accuracy could be not only explained by the nature of input data sources: e.g. the above-ground height of nDSM highlight the vertical dimension of houses, trees even cars and the nearinfrared spectrum indicates vegetation, but also attributed to decision-level fusion of CNN’s texture-based approach with multichannel spatial-spectral hand-crafted features based on the evidence combination theory.
EXTRACTING URBAN GROUND OBJECT INFORMATION FROM IMAGES AND LiDAR DATA
Authors: Yi Lina , Zhao Xuesheng , Li Luan , Zhang Guifeng
Published: August 2016

To deal with the problem of urban ground object information extraction, the paper proposes an object-oriented classification method using aerial image and LiDAR data. Firstly, we select the optimal segmentation scales of different ground objects and synthesize them to get accurate object boundaries. Then, this paper uses ReliefF algorithm to select the optimal feature combination and eliminate the Hughes phenomenon. Eventually, the multiple classifier combination method is applied to get the outcome of the classification. In order to validate the feasible of this method, this paper selects two experimental regions in Stuttgart and Germany (Region A and B, covers 0.21 km2 and 1.1 km2 respectively). The aim of the first experiment on the Region A is to get the optimal segmentation scales and classification features. The overall accuracy of the classification reaches to 93.3 %. The purpose of the experiment on region B is to validate the application-ability of this method for a large area, which is turned out to be reaches 88.4 % overall accuracy. In the end of this paper, the conclusion shows that the proposed method can be performed accurately and efficiently in terms of urban ground information extraction and be of high application value.
USING REMOTE SENSING AND GIS TECHNIQUES TO DETECT CHANGES TO THE PRINCE ALFRED HAMLET CONSERVATION AREA IN THE WESTERN CAPE, SOUTH AFRICA
Authors: Duncan P. , Lewarne M.
Published: August 2016

Understanding and identifying the spatial-temporal changes in the natural environment is crucial for monitoring and evaluating conservation efforts, as well as understanding the impact of human activities on natural resources, informing responsible land management, and promoting better decision-making. Conservation areas are often under pressure from expanding farming and related industry, invasive alien vegetation, and an ever-increasing human settlement footprint. This study focuses on detecting changes to the Prince Alfred Hamlet commonage, near Ceres in the Cape Floral Kingdom. It was chosen for its high conservation value and significance as a critical water source area. The study area includes a fast-growing human settlement footprint in a highly productive farming landscape. There are conflicting development needs as well as risks to agricultural production, and both of these threaten the integrity of the ecosystems which supply underlying services to both demands on the land. Using a multi-disciplinary approach and high-resolution satellite imagery, land use and land cover changes can be detected and classified, and the results used to support the conservation of biodiversity and wildlife, and protect our natural resources. The aim of this research is to study the efficacy of using remote sensing and GIS techniques to detect changes to critical conservation areas where disturbances can be understood, and therefore better managed and mitigated before these areas are degraded beyond repair.
UTILIZING SAR AND MULTISPECTRAL INTEGRATED DATA FOR EMERGENCY RESPONSE
Authors: Havivi S. , Schvartzman I. , Maman S. , Marinoni A. , Gamba P. , Rotman S. R. , Blumberg D. G.
Published: August 2016

Satellite images are used widely in the risk cycle to understand the exposure, refine hazard maps and quickly provide an assessment after a natural or man-made disaster. Though there are different types of satellite images (e.g. optical, radar) these have not been combined for risk assessments. The characteristics of different remote sensing data type may be extremely valuable for monitoring and evaluating the impacts of disaster events, to extract additional information thus making it available for emergency situations. To base this approach, two different change detection methods, for two different sensor's data were used: Coherence Change Detection (CCD) for SAR data and Covariance Equalization (CE) for multispectral imagery. The CCD provides an identification of the stability of an area, and shows where changes have occurred. CCD shows subtle changes with an accuracy of several millimetres to centimetres. The CE method overcomes the atmospheric effects differences between two multispectral images, taken at different times. Therefore, areas that had undergone a major change can be detected. To achieve our goals, we focused on the urban areas affected by the tsunami event in Sendai, Japan that occurred on March 11, 2011 which affected the surrounding area, coastline and inland. High resolution TerraSAR-X (TSX) and Landsat 7 images, covering the research area, were acquired for the period before and after the event. All pre-processed and processed according to each sensor. Both results, of the optical and SAR algorithms, were combined by resampling the spatial resolution of the Multispectral data to the SAR resolution. This was applied by spatial linear interpolation. A score representing the damage level in both products was assigned. The results of both algorithms, high level of damage is shown in the areas closer to the sea and shoreline. Our approach, combining SAR and multispectral images, leads to more reliable information and provides a complete scene for the emergency response following an event.
CHANGE DETECTION IN UAV VIDEO MOSAICS COMBINING A FEATURE BASED APPROACH AND EXTENDED IMAGE DIFFERENCING
Authors: Saur Günter , Krüger Wolfgang
Published: August 2016

Change detection is an important task when using unmanned aerial vehicles (UAV) for video surveillance. We address changes of short time scale using observations in time distances of a few hours. Each observation (previous and current) is a short video sequence acquired by UAV in near-Nadir view. Relevant changes are, e.g., recently parked or moved vehicles. Examples for non-relevant changes are parallaxes caused by 3D structures of the scene, shadow and illumination changes, and compression or transmission artifacts. In this paper we present (1) a new feature based approach to change detection, (2) a combination with extended image differencing (Saur et al., 2014), and (3) the application to video sequences using temporal filtering. In the feature based approach, information about local image features, e.g., corners, is extracted in both images. The label “new object” is generated at image points, where features occur in the current image and no or weaker features are present in the previous image. The label “vanished object” corresponds to missing or weaker features in the current image and present features in the previous image. This leads to two “directed” change masks and differs from image differencing where only one “undirected” change mask is extracted which combines both label types to the single label “changed object”. The combination of both algorithms is performed by merging the change masks of both approaches. A color mask showing the different contributions is used for visual inspection by a human image interpreter.
DETECTION OF HARBOURS FROM HIGH RESOLUTION REMOTE SENSING IMAGERY VIA SALIENCY ANALYSIS AND FEATURE LEARNING
Authors: Wang Yetianjian , Pan Li , Wang Dagang , Kang Yifei
Published: August 2016

Harbours are very important objects in civil and military fields. To detect them from high resolution remote sensing imagery is important in various fields and also a challenging task. Traditional methods of detecting harbours mainly focus on the segmentation of water and land and the manual selection of knowledge. They do not make enough use of other features of remote sensing imagery and often fail to describe the harbours completely. In order to improve the detection, a new method is proposed. First, the image is transformed to Hue, Saturation, Value (HSV) colour space and saliency analysis is processed via the generation and enhancement of the co-occurrence histogram to help detect and locate the regions of interest (ROIs) that is salient and may be parts of the harbour. Next, SIFT features are extracted and feature learning is processed to help represent the ROIs. Then, by using classified feature of the harbour, a classifier is trained and used to check the ROIs to find whether they belong to the harbour. Finally, if the ROIs belong to the harbour, a minimum bounding rectangle is formed to include all the harbour ROIs and detect and locate the harbour. The experiment on high resolution remote sensing imagery shows that the proposed method performs better than other methods in precision of classifying ROIs and accuracy of completely detecting and locating harbours.
AN UNSUPERVISED CHANGE DETECTION BASED ON TEST STATISTIC AND KI FROM MULTI-TEMPORAL AND FULL POLARIMETRIC SAR IMAGES
Authors: Zhao J. Q. , Yang J. , Li P. X. , Liu M. Y. , Shi Y. M.
Published: August 2016

Accurate and timely change detection of Earth’s surface features is extremely important for understanding relationships and interactions between people and natural phenomena. Many traditional methods of change detection only use a part of polarization information and the supervised threshold selection. Those methods are insufficiency and time-costing. In this paper, we present a novel unsupervised change-detection method based on quad-polarimetric SAR data and automatic threshold selection to solve the problem of change detection. First, speckle noise is removed for the two registered SAR images. Second, the similarity measure is calculated by the test statistic, and automatic threshold selection of KI is introduced to obtain the change map. The efficiency of the proposed method is demonstrated by the quad-pol SAR images acquired by Radarsat-2 over Wuhan of China.
GEOSPATIAL ANALYSIS OF NEAR-SURFACE SOIL MOISTURE TIME SERIES DATA OVER INDIAN REGION
Authors: Berwal P. , Murthy C. S. , Raju P.V. , Sesha Sai M. V. R.
Published: August 2016

The present study has developed the time series database surface soil moisture over India, for June, July and August months for the period of 20 years from 1991 to 2010, using data products generated under Climate Change Initiative Programme of European Space Agency. These three months represent the crop sowing period in the prime cropping season in the country and the soil moisture data during this period is highly useful to detect the drought conditions and assess the drought impact. The time series soil moisture data which is in 0.25 degree spatial resolution was analyzed to generate different indicators. Rainfall data of same spatial resolution for the same period, generated by India Meteorological Department was also procured and analyzed. Geospatial analysis of soil moisture and rainfall derived indicators was carried out to study (1) inter annual variability of soil moisture and rainfall, (2) soil moisture deviations from normal during prominent drought years, (3) soil moisture and rainfall correlations and (4) drought exposure based on soil moisture and rainfall variability. The study has successfully demonstrated the potential of these soil moisture time series data sets for generating regional drought surveillance information products, drought hazard mapping, drought exposure analysis and detection of drought sensitive areas in the crop planting period.
SOME ASPECTS OF SATELLITE IMAGERY INTEGRATION FROM EROS B AND LANDSAT 8
Authors: Fryskowska A. , Wojtkowska M. , Delis P. , Grochala A.
Published: August 2016

The Landsat 8 satellite which was launched in 2013 is a next generation of the Landsat remote sensing satellites series. It is equipped with two new sensors: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). What distinguishes this satellite from the previous is four new bands (coastal aerosol, cirrus and two thermal infrared TIRS bands). Similar to its antecedent, Landsat 8 records electromagnetic radiation in a panchromatic band at a range of 0.5‐0.9 μm with a spatial resolution equal to 15 m. In the paper, multispectral imagery integration capabilities of Landsat 8 with data from the new high resolution panchromatic EROS B satellite are analyzed. The range of panchromatic band for EROS B is 0.4‐0.9 μm and spatial resolution is 0.7 m. Research relied on improving the spatial resolution of natural color band combinations (bands: 4,3,2) and of desired false color band composition of Landsat 8 satellite imagery. For this purpose, six algorithms have been tested: Brovey’s, Mulitplicative, PCA, IHS, Ehler's, HPF. On the basis of the visual assessment, it was concluded that the best results of multispectral and panchromatic image integration, regardless land cover, are obtained for the multiplicative method. These conclusions were confirmed by statistical analysis using correlation coefficient, ERGAS and R-RMSE indicators.
HYPERSPECTRAL TRANSFORMATION FROM EO-1 ALI IMAGERY USING PSEUDO-HYPERSPECTRAL IMAGE SYNTHESIS ALGORITHM
Authors: Hoang Nguyen Tien , Koike Katsuaki
Published: August 2016

Hyperspectral remote sensing is more effective than multispectral remote sensing in many application fields because of having hundreds of observation bands with high spectral resolution. However, hyperspectral remote sensing resources are limited both in temporal and spatial coverage. Therefore, simulation of hyperspectral imagery from multispectral imagery with a small number of bands must be one of innovative topics. Based on this background, we have recently developed a method, Pseudo-Hyperspectral Image Synthesis Algorithm (PHISA), to transform Landsat imagery into hyperspectral imagery using the correlation of reflectance at the corresponding bands between Landsat and EO-1 Hyperion data. This study extends PHISA to simulate pseudo-hyperspectral imagery from EO-1 ALI imagery. The pseudo-hyperspectral imagery has the same number of bands as that of high-quality Hyperion bands and the same swath width as ALI scene. The hyperspectral reflectance data simulated from the ALI data show stronger correlation with the original Hyperion data than the one simulated from Landsat data. This high correlation originates from the concurrent observation by the ALI and Hyperion sensors that are on-board the same satellite. The accuracy of simulation results are verified by a statistical analysis and a surface mineral mapping. With a combination of the advantages of both ALI and Hyperion image types, the pseudo-hyperspectral imagery is proved to be useful for detailed identification of minerals for the areas outside the Hyperion coverage.
ON THE CHALLENGES IN STEREOGRAMMETRIC FUSION OF SAR AND OPTICAL IMAGERY FOR URBAN AREAS
Authors: Schmitt M. , Zhu X. X.
Published: August 2016

This paper discusses the challenges arising if SAR and optical imagery shall be fused for stereogrammetric 3D analysis of urban areas. In this context, a concept for SAR and optical data fusion is presented, which is meant to enable the reconstruction of urban topography independent of the type of the available data. This fusion is modelled in a voxelized object space, from which 3D hypotheses are projected into the available datasets. Among those hypotheses then the one showing the greatest SAR-optical similarity is chosen to be the reconstructed 3D point. Within first experiments, it is shown that the determination of similarity between high-resolution SAR and optical images is the major challenge within the framework of the proposed concept. After this challenge has been solved, the proposed method is expected to allow 3D reconstruction of urban areas from SAR-optical stereogrammetry for the first time. It is expected to be beneficial, e.g., for rapid mapping tasks in disaster situations where optical images may be available from geodata archives, but instantaneous data can only be provided by daylight- and weather-independent SAR sensors.
MODEL ACCURACY COMPARISON FOR HIGH RESOLUTION INSAR COHERENCE STATISTICS OVER URBAN AREAS
Authors: Zhang Yue , Fu Kun , Sun Xian , Xu Guangluan , Wang Hongqi
Published: August 2016

The interferometric coherence map derived from the cross-correlation of two complex registered synthetic aperture radar (SAR) images is the reflection of imaged targets. In many applications, it can act as an independent information source, or give additional information complementary to the intensity image. Specially, the statistical properties of the coherence are of great importance in land cover classification, segmentation and change detection. However, compared to the amount of work on the statistical characters of SAR intensity, there are quite fewer researches on interferometric SAR (InSAR) coherence statistics. And to our knowledge, all of the existing work that focuses on InSAR coherence statistics, models the coherence with Gaussian distribution with no discrimination on data resolutions or scene types. But the properties of coherence may be different for different data resolutions and scene types. In this paper, we investigate on the coherence statistics for high resolution data over urban areas, by making a comparison of the accuracy of several typical statistical models. Four typical land classes including buildings, trees, shadow and roads are selected as the representatives of urban areas. Firstly, several regions are selected from the coherence map manually and labelled with their corresponding classes respectively. Then we try to model the statistics of the pixel coherence for each type of region, with different models including Gaussian, Rayleigh, Weibull, Beta and Nakagami. Finally, we evaluate the model accuracy for each type of region. The experiments on TanDEM-X data show that the Beta model has a better performance than other distributions.