bluuebunny/crossref_metadata_2025 huggingface dataset:
ASSESSMENT OF CROPPING SYSTEM DIVERSITY IN THE FERGANA VALLEY THROUGH IMAGE FUSION OF LANDSAT 8 AND SENTINEL-1
Authors: Dimov D. , Kuhn J. , Conrad C.
Published: August 2016

In the transitioning agricultural societies of the world, food security is an essential element of livelihood and economic development with the agricultural sector very often being the major employment factor and income source. Rapid population growth, urbanization, pollution, desertification, soil degradation and climate change pose a variety of threats to a sustainable agricultural development and can be expressed as agricultural vulnerability components. Diverse cropping patterns may help to adapt the agricultural systems to those hazards in terms of increasing the potential yield and resilience to water scarcity. Thus, the quantification of crop diversity using indices like the Simpson Index of Diversity (SID) e.g. through freely available remote sensing data becomes a very important issue. This however requires accurate land use classifications. In this study, the focus is set on the cropping system diversity of garden plots, summer crop fields and orchard plots which are the prevalent agricultural systems in the test area of the Fergana Valley in Uzbekistan. In order to improve the accuracy of land use classification algorithms with low or medium resolution data, a novel processing chain through the hitherto unique fusion of optical and SAR data from the Landsat 8 and Sentinel-1 platforms is proposed. The combination of both sensors is intended to enhance the object´s textural and spectral signature rather than just to enhance the spatial context through pansharpening. It could be concluded that the Ehlers fusion algorithm gave the most suitable results. Based on the derived image fusion different object-based image classification algorithms such as SVM, Naïve Bayesian and Random Forest were evaluated whereby the latter one achieved the highest classification accuracy. Subsequently, the SID was applied to measure the diversification of the three main cropping systems.
A NEW SPATIAL AND TEMPORAL FUSION MODEL
Authors: Wang Jing , Huang Bo
Published: August 2016

As Moderate Resolution Imaging Spectroradiometer (MODIS) and Enhanced Thematic Mapper Plus (ETM+) has a tradeoff between the high temporal resolution and high spatial resolution, this paper proposed a spatial and temporal model with auto-regression error correction (AREC) method to blend the two types of images in order to obtain the composed image with both high spatial and temporal resolution. Experiments and validation were conducted on a data set located in Shenzhen, China and compared with Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) in several objective indexes and visual analysis. It was found that AREC could effectively predict the land cover changes and the fusion results had better performances versus the ones of STARFM.
A PLUGIN TO INTERFACE OPENMODELLER FROM QGIS FOR SPECIES' POTENTIAL DISTRIBUTION MODELLING
Authors: Becker Daniel , Willmes Christian , Bareth Georg , Weniger Gerd-Christian
Published: August 2016

This contribution describes the development of a plugin for the geographic information system QGIS to interface the openModeller software package. The aim is to use openModeller to generate species’ potential distribution models for various archaeological applications (site catchment analysis, for example). Since the usage of openModeller’s command-line interface and configuration files can be a bit inconvenient, an extension of the QGIS user interface to handle these tasks, in combination with the management of the geographic data, was required. The implementation was realized in Python using PyQGIS and PyQT. The plugin, in combination with QGIS, handles the tasks of managing geographical data, data conversion, generation of configuration files required by openModeller and compilation of a project folder. The plugin proved to be very helpful with the task of compiling project datasets and configuration files for multiple instances of species occurrence datasets and the overall handling of openModeller. In addition, the plugin is easily extensible to take potential new requirements into account in the future.
INFLUENCE OF THE VIEWING GEOMETRY WITHIN HYPERSPECTRAL IMAGES RETRIEVED FROM UAV SNAPSHOT CAMERAS
Authors: Aasen Helge
Published: August 2016

Hyperspectral data has great potential for vegetation parameter retrieval. However, due to angular effects resulting from different sun-surface-sensor geometries, objects might appear differently depending on the position of an object within the field of view of a sensor. Recently, lightweight snapshot cameras have been introduced, which capture hyperspectral information in two spatial and one spectral dimension and can be mounted on unmanned aerial vehicles.

 This study investigates the influence of the different viewing geometries within an image on the apparent hyperspectral reflection retrieved by these sensors. Additionally, it is evaluated how hyperspectral vegetation indices like the NDVI are effected by the angular effects within a single image and if the viewing geometry influences the apparent heterogeneity with an area of interest. The study is carried out for a barley canopy at booting stage.

 The results show significant influences of the position of the area of interest within the image. The red region of the spectrum is more influenced by the position than the near infrared. The ability of the NDVI to compensate these effects was limited to the capturing positions close to nadir. The apparent heterogeneity of the area of interest is the highest close to a nadir.
ESTIMATING CORN YIELD IN THE UNITED STATES WITH MODIS EVI AND MACHINE LEARNING METHODS
Authors: Kuwata K. , Shibasaki R.
Published: August 2016

Satellite remote sensing is commonly used to monitor crop yield in wide areas. Because many parameters are necessary for crop yield estimation, modelling the relationships between parameters and crop yield is generally complicated. Several methodologies using machine learning have been proposed to solve this issue, but the accuracy of county-level estimation remains to be improved. In addition, estimating county-level crop yield across an entire country has not yet been achieved. In this study, we applied a deep neural network (DNN) to estimate corn yield. We evaluated the estimation accuracy of the DNN model by comparing it with other models trained by different machine learning algorithms. We also prepared two time-series datasets differing in duration and confirmed the feature extraction performance of models by inputting each dataset. As a result, the DNN estimated county-level corn yield for the entire area of the United States with a determination coefficient (R2) of 0.780 and a root mean square error (RMSE) of 18.2 bushels/acre. In addition, our results showed that estimation models that were trained by a neural network extracted features from the input data better than an existing machine learning algorithm.
TOWARDS A REMOTE SENSING BASED ASSESSMENT OF LAND SUSCEPTIBILITY TO DEGRADATION: EXAMINING SEASONAL VARIATION IN LAND USE-LAND COVER FOR MODELLING LAND DEGRADATION IN A SEMI-ARID CONTEXT
Authors: Mashame Gofamodimo , Akinyemi Felicia
Published: August 2016

Land degradation (LD) is among the major environmental and anthropogenic problems driven by land use-land cover (LULC) and climate change worldwide. For example, poor LULC practises such as deforestation, livestock overstocking, overgrazing and arable land use intensification on steep slopes disturbs the soil structure leaving the land susceptible to water erosion, a type of physical land degradation. Land degradation related problems exist in Sub-Saharan African countries such as Botswana which is semi-arid in nature. LULC and LD linkage information is still missing in many semi-arid regions worldwide.Mapping seasonal LULC is therefore very important in understanding LULC and LD linkages. This study assesses the impact of seasonal LULC variation on LD utilizing Remote Sensing (RS) techniques for Palapye region in Central District, Botswana. LULC classes for the dry and rainy seasons were classified using LANDSAT 8 images at Level I according to the Food and Agriculture Organization (FAO) International Organization of Standardization (ISO) code 19144. Level I consists of 10 LULC classes. The seasonal variations in LULC are further related to LD susceptibility in the semi-arid context. The results suggest that about 985 km² (22%) of the study area is susceptible to LD by water, major LULC types affected include: cropland, paved/rocky material, bare land, built-up area, mining area, and water body. Land degradation by water susceptibility due to seasonal land use-land cover variations is highest in the east of the study area where there is high cropland to bare land conversion.
COASTLINE EXTRACTION FROM AERIAL IMAGES BASED ON EDGE DETECTION
Authors: Paravolidakis V. , Moirogiorgou K. , Ragia L. , Zervakis M. , Synolakis C.
Published: August 2016

Nowadays coastline extraction and tracking of its changes become of high importance because of the climate change, global warming and rapid growth of human population. Coastal areas play a significant role for the economy of the entire region. In this paper we propose a new methodology for automatic extraction of the coastline using aerial images. A combination of a four step algorithm is used to extract the coastline in a robust and generalizable way. First, noise distortion is reduced in order to ameliorate the input data for the next processing steps. Then, the image is segmented into two regions, land and sea, through the application of a local threshold to create the binary image. The result is further processed by morphological operators with the aim that small objects are being eliminated and only the objects of interest are preserved. Finally, we perform edge detection and active contours fitting in order to extract and model the coastline. These algorithmic steps are illustrated through examples, which demonstrate the efficacy of the proposed methodology.
AUTOMATED WETLAND DELINEATION FROM MULTI-FREQUENCY AND MULTI-POLARIZED SAR IMAGES IN HIGH TEMPORAL AND SPATIAL RESOLUTION
Authors: Moser L. , Schmitt A. , Wendleder A.
Published: August 2016

Water scarcity is one of the main challenges posed by the changing climate. Especially in semi-arid regions where water reservoirs are filled during the very short rainy season, but have to store enough water for the extremely long dry season, the intelligent handling of water resources is vital. This study focusses on Lac Bam in Burkina Faso, which is the largest natural lake of the country and of high importance for the local inhabitants for irrigated farming, animal watering, and extraction of water for drinking and sanitation. With respect to the competition for water resources an independent area-wide monitoring system is essential for the acceptance of any decision maker. The following contribution introduces a weather and illumination independent monitoring system for the automated wetland delineation with a high temporal (about two weeks) and a high spatial sampling (about five meters). The similarities of the multispectral and multi-polarized SAR acquisitions by RADARSAT-2 and TerraSAR-X are studied as well as the differences. The results indicate that even basic approaches without pre-classification time series analysis or post-classification filtering are already enough to establish a monitoring system of prime importance for a whole region.
DEVELOPMENT OF A MULTI-SITE AND MULTI-DEVICE WEBGIS-BASED TOOL FOR TIDAL CURRENT ENERGY DEVELOPMENT
Authors: Ang M. R. C. O. , Panganiban I. K. , Mamador C. C. , De Luna O. D. G. , Bausas M. D. , Cruz J. P.
Published: August 2016

A multi-site, multi-device and multi-criteria decision support tool designed to support the development of tidal current energy in the Philippines was developed. Its platform is based on Geographic Information Systems (GIS) which allows for the collection, storage, processing, analyses and display of geospatial data. Combining GIS tools with open source web development applications, it becomes a webGIS-based marine spatial planning tool. To date, the webGIS-based tool displays output maps and graphs of power and energy density, site suitability and site-device analysis. It enables stakeholders and the public easy access to the results of tidal current energy resource assessments and site suitability analyses. Results of the initial development showed that it is a promising decision support tool for ocean renewable energy project developments.
EVALUATION OF VERTICAL LACUNARITY PROFILES IN FORESTED AREAS USING AIRBORNE LASER SCANNING POINT CLOUDS
Authors: Székely B. , Kania A. , Standovár T. , Heilmeier H.
Published: August 2016

The horizontal variation and vertical layering of the vegetation are important properties of the canopy structure determining the habitat; three-dimensional (3D) distribution of objects (shrub layers, understory vegetation, etc.) is related to the environmental factors (e.g., illumination, visibility). It has been shown that gaps in forests, mosaic-like structures are essential to biodiversity; various methods have been introduced to quantify this property. As the distribution of gaps in the vegetation is a multi-scale phenomenon, in order to capture it in its entirety, scale-independent methods are preferred; one of these is the calculation of lacunarity.

 We used Airborne Laser Scanning point clouds measured over a forest plantation situated in a former floodplain. The flat topographic relief ensured that the tree growth is independent of the topographic effects. The tree pattern in the plantation crops provided various quasi-regular and irregular patterns, as well as various ages of the stands. The point clouds were voxelized and layers of voxels were considered as images for two-dimensional input. These images calculated for a certain vicinity of reference points were taken as images for the computation of lacunarity curves, providing a stack of lacunarity curves for each reference points. These sets of curves have been compared to reveal spatial changes of this property. As the dynamic range of the lacunarity values is very large, the natural logarithms of the values were considered. Logarithms of lacunarity functions show canopy-related variations, we analysed these variations along transects. The spatial variation can be related to forest properties and ecology-specific aspects.
AUTOMATIC ORIENTATION AND MOSAICKING OF ARCHIVED AERIAL PHOTOGRAPHY USING STRUCTURE FROM MOTION
Authors: Gonçalves J. A.
Published: August 2016

Aerial photography has been acquired regularly for topographic mapping since the decade of 1930. In Portugal there are several archives of aerial photos in national mapping institutes, as well as in local authorities, containing a total of nearly one hundred thousand photographs, mainly from the 1940s, 1950s and some from 1930s. These data sets provide important information about the evolution of the territory, for environment and agricultural studies, land planning, and many other examples. There is an interest in making these aerial coverages available in the form of orthorectified mosaics for integration in a GIS.

 The orthorectification of old photographs may pose several difficulties. Required data about the camera and lens system used, such as the focal distance, fiducial marks coordinates or distortion parameters may not be available, making it difficult to process these data in conventional photogrammetric software.

 This paper describes an essentially automatic methodology for orientation, orthorectification and mosaic composition of blocks of old aerial photographs, using Agisoft Photoscan structure from motion software. The operation sequence is similar to the processing of UAV imagery. The method was applied to photographs from 1947 and 1958, provided by the Portuguese Army Geographic Institute. The orientation was done with GCPs collected from recent orthophototos and topographic maps. This may be a difficult task, especially in urban areas that went through many changes. Residuals were in general below 1 meter. The agreement of the orthomosaics with recent orthophotos and GIS vector data was in general very good. The process is relatively fast and automatic, and can be considered in the processing of full coverages of old aerial photographs.
UNDERWATER CALIBRATION OF DOME PORT PRESSURE HOUSINGS
Authors: Nocerino E. , Menna F. , Fassi F. , Remondino F.
Published: August 2016

Underwater photogrammetry using consumer grade photographic equipment can be feasible for different applications, e.g. archaeology, biology, industrial inspections, etc. The use of a camera underwater can be very different from its terrestrial use due to the optical phenomena involved. The presence of the water and camera pressure housing in front of the camera act as additional optical elements. Spherical dome ports are difficult to manufacture and consequently expensive but at the same time they are the most useful for underwater photogrammetry as they keep the main geometric characteristics of the lens unchanged. Nevertheless, the manufacturing and alignment of dome port pressure housing components can be the source of unexpected changes of radial and decentring distortion, source of systematic errors that can influence the final 3D measurements. The paper provides a brief introduction of underwater optical phenomena involved in underwater photography, then presents the main differences between flat and dome ports to finally discuss the effect of manufacturing on 3D measurements in two case studies.
TOWARDS EFFICIENCY OF OBLIQUE IMAGES ORIENTATION
Authors: Ostrowski W. , Bakuła K.
Published: August 2016

Many papers on both theoretical aspects of bundle adjustment of oblique images and new operators for detecting tie points on oblique images have been written. However, only a few achievements presented in the literature were practically implemented in commercial software. In consequence often aerial triangulation is performed either for nadir images obtained simultaneously with oblique photos or bundle adjustment for separate images captured in different directions. The aim of this study was to investigate how the orientation of oblique images can be carried out effectively in commercial software based on the structure from motion technology. The main objective of the research was to evaluate the impact of the orientation strategy on both duration of the process and accuracy of photogrammetric 3D products. Two, very popular software: Pix4D and Agisoft Photoscan were tested and two approaches for image blocks were considered. The first approach based only on oblique images collected in four directions and the second approach included nadir images. In this study, blocks for three test areas were analysed. Oblique images were collected with medium-format cameras in maltan cross configuration with registration of GNSS and INS data. As a reference both check points and digital surface models from airborne laser scanning were used.
DESIGN AND IMPLEMENTATION OF A LOW-COST UAV-BASED MULTI-SENSOR PAYLOAD FOR RAPID-RESPONSE MAPPING APPLICATIONS
Authors: Sakr M. , Lari Z. , El-Sheimy N.
Published: August 2016

The main objective of this paper is to investigate the potential of using Unmanned Aerial Vehicles (UAVs) as a platform to collect geospatial data for rapid response applications, especially in hard-to-access and hazardous areas. The UAVs are low-cost mapping vehicles, and they are easy to handle and deploy in-field. These characteristics make UAVs ideal candidates for rapid-response and disaster mitigation scenarios. The majority of the available UAV systems are not capable of real-time/near real-time data processing. This paper introduces a low-cost UAV-based multi-sensor mapping payload which supports real-time processing and can be effectively used in rapid-response applications. The paper introduces the main components of the system, and provides an overview of the proposed payload architecture. Then, it introduces the implementation details of the major building blocks of the system. Finally, the paper presents our conclusions and the future work, in order to achieve real-time/near real-time data processing and product delivery capabilities.
ARCHAEOLOGICAL DOCUMENTATION OF A DEFUNCT IRAQI TOWN
Authors: Šedina J. , Pavelka K. , Housarová E.
Published: August 2016

The subject of this article is the possibilities of the documentation of a defunct town from the Pre-Islamic period to Early Islamic period. This town is located near the town Makhmur in Iraq. The Czech archaeological mission has worked at this dig site. This Cultural Heritage site is threatened by war because in the vicinity are positions of ISIS. For security reasons, the applicability of Pleiades satellite data has been tested. Moreover, this area is a no-fly zone. However, the DTM created from stereo-images was insufficient for the desired application in archeology. The subject of this paper is the testing of the usability of RPAS technology and terrestrial photogrammetry for documentation of the remains of buildings. RPAS is a very fast growing technology that combines the advantages of aerial photogrammetry and terrestrial photogrammetry. A probably defunct church is a sample object.
UAV ONBOARD GPS IN POSITIONING DETERMINATION
Authors: Tahar K. N. , Kamarudin S. S.
Published: August 2016

The establishment of ground control points is a critical issue in mapping field, especially for large scale mapping. The fast and rapid technique for ground control point’s establishment is very important for small budget projects. UAV onboard GPS has the ability to determine the point positioning. The objective of this research is to assess the accuracy of unmanned aerial vehicle onboard global positioning system in positioning determination. Therefore, this research used UAV onboard GPS as an alternative to determine the point positioning at the selected area. UAV is one of the powerful tools for data acquisition and it is used in many applications all over the world. This research concentrates on the error contributed from the UAV onboard GPS during observation. There are several points that have been used to study the pattern of positioning error. All errors were analyzed in world geodetic system 84- coordinate system, which is the basic coordinate system used by the global positioning system. Based on this research, the result of UAV onboard GPS positioning could be used in ground control point establishment with the specific error. In conclusion, accurate GCP establishment could be achieved using UAV onboard GPS by applying a specific correction based on this research.
AN INTEGRATIVE OBJECT-BASED IMAGE ANALYSIS WORKFLOW FOR UAV IMAGES
Authors: Yu Huai , Yan Tianheng , Yang Wen , Zheng Hong
Published: August 2016

In this work, we propose an integrative framework to process UAV images. The overall process can be viewed as a pipeline consisting of the geometric and radiometric corrections, subsequent panoramic mosaicking and hierarchical image segmentation for later Object Based Image Analysis (OBIA). More precisely, we first introduce an efficient image stitching algorithm after the geometric calibration and radiometric correction, which employs a fast feature extraction and matching by combining the local difference binary descriptor and the local sensitive hashing. We then use a Binary Partition Tree (BPT) representation for the large mosaicked panoramic image, which starts by the definition of an initial partition obtained by an over-segmentation algorithm, i.e., the simple linear iterative clustering (SLIC). Finally, we build an object-based hierarchical structure by fully considering the spectral and spatial information of the super-pixels and their topological relationships. Moreover, an optimal segmentation is obtained by filtering the complex hierarchies into simpler ones according to some criterions, such as the uniform homogeneity and semantic consistency. Experimental results on processing the post-seismic UAV images of the 2013 Ya’an earthquake demonstrate the effectiveness and efficiency of our proposed method.
RANDOM FOREST AND OBJECTED-BASED CLASSIFICATION FOR FOREST PEST EXTRACTION FROM UAV AERIAL IMAGERY
Authors: Yuan Yi , Hu Xiangyun
Published: August 2016

Forest pest is one of the most important factors affecting the health of forest. However, since it is difficult to figure out the pest areas and to predict the spreading ways just to partially control and exterminate it has not effective enough so far now. The infected areas by it have continuously spreaded out at present. Thus the introduction of spatial information technology is highly demanded. It is very effective to examine the spatial distribution characteristics that can establish timely proper strategies for control against pests by periodically figuring out the infected situations as soon as possible and by predicting the spreading ways of the infection. Now, with the UAV photography being more and more popular, it has become much cheaper and faster to get UAV images which are very suitable to be used to monitor the health of forest and detect the pest. This paper proposals a new method to effective detect forest pest in UAV aerial imagery. For an image, we segment it to many superpixels at first and then we calculate a 12-dimension statistical texture information for each superpixel which are used to train and classify the data. At last, we refine the classification results by some simple rules. The experiments show that the method is effective for the extraction of forest pest areas in UAV images.
FROM UAS DATA ACQUISITION TO ACTIONABLE INFORMATION – HOW AN END-TO-END SOLUTION HELPS OIL PALM PLANTATION OPERATORS TO PERFORM A MORE SUSTAINABLE PLANTATION MANAGEMENT
Authors: Hoffmann C. , Weise C. , Koch T. , Pauly K.
Published: August 2016

Palm oil represents the most efficient oilseed crop in the world but the production of palm oil involves plantation operations in one of the most fragile environments - the tropical lowlands. Deforestation, the drying-out of swampy lowlands and chemical fertilizers lead to environmental problems that are putting pressure on this industry. Unmanned aircraft systems (UAS) together with latest photogrammetric processing and image analysis capabilities represent an emerging technology that was identified to be suitable to optimize oil palm plantation operations.

 This paper focuses on two key elements of a UAS-based oil palm monitoring system. The first is the accuracy of the acquired data that is necessary to achieve meaningful results in later analysis steps. High performance GNSS technology was utilized to achieve those accuracies while decreasing the demand for cost-intensive GCP measurements.

 The second key topic is the analysis of the resulting data in order to optimize plantation operations. By automatically extracting information on a block level as well as on a single-tree level, operators can utilize the developed application to increase their productivity.

 The research results describe how operators can successfully make use of a UAS-based solution together with the developed software solution to improve their efficiency in oil palm plantation management.
COMPACT HYPERSPECTRAL IMAGING SYSTEM (COSI) FOR SMALL REMOTELY PILOTED AIRCRAFT SYSTEMS (RPAS) – SYSTEM OVERVIEW AND FIRST PERFORMANCE EVALUATION RESULTS
Authors: Sima A. A. , Baeck P. , Nuyts D. , Delalieux S. , Livens S. , Blommaert J. , Delauré B. , Boonen M.
Published: August 2016

This paper gives an overview of the new COmpact hyperSpectral Imaging (COSI) system recently developed at the Flemish Institute for Technological Research (VITO, Belgium) and suitable for remotely piloted aircraft systems. A hyperspectral dataset captured from a multirotor platform over a strawberry field is presented and explored in order to assess spectral bands co-registration quality. Thanks to application of line based interference filters deposited directly on the detector wafer the COSI camera is compact and lightweight (total mass of 500g), and captures 72 narrow (FWHM: 5nm to 10 nm) bands in the spectral range of 600-900 nm. Covering the region of red edge (680 nm to 730 nm) allows for deriving plant chlorophyll content, biomass and hydric status indicators, making the camera suitable for agriculture purposes. Additionally to the orthorectified hypercube digital terrain model can be derived enabling various analyses requiring object height, e.g. plant height in vegetation growth monitoring. Geometric data quality assessment proves that the COSI camera and the dedicated data processing chain are capable to deliver very high resolution data (centimetre level) where spectral information can be correctly derived. Obtained results are comparable or better than results reported in similar studies for an alternative system based on the Fabry–Pérot interferometer.
COPERNICUS – PRACTICE OF DAILY LIFE IN A NATIONAL MAPPING AGENCY?
Authors: Wiatr T. , Suresh G. , Gehrke R. , Hovenbitzer M.
Published: August 2016

Copernicus is an European system created for Earth observation and monitoring. It consists of a set of Earth observation satellites and in-situ sensors that provide geo-information that are used, through a set of Copernicus services, for applications related to the environment and global security. The main services of the Copernicus programme address six thematic areas: land, marine, atmosphere, climate change, emergency management and security. In Germany, there is a national service team of Copernicus service coordinators, who are responsible for the national development of the Copernicus services and for providing user-specific information about the Copernicus processes. These coordinators represent the contact points for all the programmes and services concerning their respective Copernicus theme. To publish information about Copernicus, national conferences and workshops are organised. Many people are involved in planning the continuous process of bringing the information to public authorities, research institutes and commercial companies. The Federal Agency for Cartography and Geodesy (Bundesamt für Kartographie und Geodäsie, BKG) is one such organisation, and is mainly responsible for the national land monitoring service of Copernicus. To make use of the freely available data from the Copernicus programme, the Federal Agency for Cartography and Geodesy is currently developing new applications and projects in the field of remote sensing and land monitoring. These projects can be used by other public authorities as examples on how to use the Copernicus data and services for their individual demands and requirements. Copernicus data and services are currently not very commonly used in the daily routine of the national mapping agencies, but they will soon be.
REDUCTION OF STRIPING NOISE IN OVERLAPPING LIDAR INTENSITY DATA BY RADIOMETRIC NORMALIZATION
Authors: Yan Wai Yeung , Shaker Ahmed
Published: August 2016

To serve seamless mapping, airborne LiDAR data are usually collected with multiple parallel strips with one or two cross strip(s). Nevertheless, the overlapping regions of LiDAR data strips are usually found with unbalanced intensity values, resulting in the appearance of stripping noise. Despite that physical intensity correction methods are recently proposed, some of the system and environmental parameters are assumed as constant or not disclosed, leading to such an intensity discrepancy. This paper presents a new normalization technique to adjust the radiometric misalignment found in the overlapping LiDAR data strips. The normalization technique is built upon a second-order polynomial function fitted on the joint histogram plot, which is generated with a set of pairwise closest data points identified within the overlapping region. The method was tested on Teledyne Optech’s Gemini dataset (at 1064 nm wavelength), where the LiDAR intensity data were first radiometrically corrected based on the radar (range) equation. Five land cover features were selected to evaluate the coefficient of variation (cv) of the intensity values before and after implementing the proposed method. Reduction of cv was found by 19% to 59% in the Gemini dataset, where the striping noise was significantly reduced in the radiometrically corrected and normalized intensity data. The Gemini dataset was also used to conduct land cover classification, and the overall accuracy yielded a notable improvement of 9% to 18%. As a result, LiDAR intensity data should be pre-processed with radiometric correction and normalization prior to any data manipulation.
ASSESSMENT OF FOUR TYPICAL TOPOGRAPHIC CORRECTIONS IN LANDSAT TM DATA FOR SNOW COVER AREAS
Authors: Zhou Yan , Jiang He , Wang Zhe , Yang Xiaoxia , Geng Erhui
Published: August 2016

The accuracy of snow cover information extraction in remote-sensing images dependent on a variety of factors, especially in mountain area with complex terrain. This paper aims at analyzing the accuracy of snow cover information extraction from remot esensing images, using Landsat5 TM images and DEM data, with the study area of Xinjiang Tianshan, measuring topographic correction effects of Cosine correction, C correction, SCS correction, and SCS + C correction from four aspects: visual comparison, standard deviation, correlation analysis and histogram, then extract snow cover area for study area. Results showed that C correction and SCS+C correction performed better among four classic terrain correction models, which indicated changes in snow pixel rat io after correction with variation range of 2% , and correlation coefficient of each band is highest before and after correction.
LOW COST AND EFFICIENT 3D INDOOR MAPPING USING MULTIPLE CONSUMER RGB-D CAMERAS
Authors: Chen C. , Yang B. S. , Song S.
Published: August 2016

Driven by the miniaturization, lightweight of positioning and remote sensing sensors as well as the urgent needs for fusing indoor and outdoor maps for next generation navigation, 3D indoor mapping from mobile scanning is a hot research and application topic. The point clouds with auxiliary data such as colour, infrared images derived from 3D indoor mobile mapping suite can be used in a variety of novel applications, including indoor scene visualization, automated floorplan generation, gaming, reverse engineering, navigation, simulation and etc. State-of-the-art 3D indoor mapping systems equipped with multiple laser scanners product accurate point clouds of building interiors containing billions of points. However, these laser scanner based systems are mostly expensive and not portable. Low cost consumer RGB-D Cameras provides an alternative way to solve the core challenge of indoor mapping that is capturing detailed underlying geometry of the building interiors. Nevertheless, RGB-D Cameras have a very limited field of view resulting in low efficiency in the data collecting stage and incomplete dataset that missing major building structures (e.g. ceilings, walls). Endeavour to collect a complete scene without data blanks using single RGB-D Camera is not technic sound because of the large amount of human labour and position parameters need to be solved. To find an efficient and low cost way to solve the 3D indoor mapping, in this paper, we present an indoor mapping suite prototype that is built upon a novel calibration method which calibrates internal parameters and external parameters of multiple RGB-D Cameras. Three Kinect sensors are mounted on a rig with different view direction to form a large field of view. The calibration procedure is three folds: 1, the internal parameters of the colour and infrared camera inside each Kinect are calibrated using a chess board pattern, respectively; 2, the external parameters between the colour and infrared camera inside each Kinect are calibrated using a chess board pattern; 3, the external parameters between every Kinect are firstly calculated using a pre-set calibration field and further refined by an iterative closet point algorithm. Experiments are carried out to validate the proposed method upon RGB-D datasets collected by the indoor mapping suite prototype. The effectiveness and accuracy of the proposed method is evaluated by comparing the point clouds derived from the prototype with ground truth data collected by commercial terrestrial laser scanner at ultra-high density. The overall analysis of the results shows that the proposed method achieves seamless integration of multiple point clouds form different RGB-D cameras collected at 30 frame per second.
NATIONAL GUIDELINES FOR DIGITAL CAMERA SYSTEMS CERTIFICATION
Authors: Yaron Yaron , Keinan Eran , Benhamu Moshe , Regev Ronen , Zalmanzon Garry
Published: August 2016

Digital camera systems are a key component in the production of reliable, geometrically accurate, high-resolution geospatial products. These systems have replaced film imaging in photogrammetric data capturing. Today, we see a proliferation of imaging sensors collecting photographs in different ground resolutions, spectral bands, swath sizes, radiometric characteristics, accuracies and carried on different mobile platforms. In addition, these imaging sensors are combined with navigational tools (such as GPS and IMU), active sensors such as laser scanning and powerful processing tools to obtain high quality geospatial products. The quality (accuracy, completeness, consistency, etc.) of these geospatial products is based on the use of calibrated, high-quality digital camera systems.

 The new survey regulations of the state of Israel specify the quality requirements for each geospatial product including: maps at different scales and for different purposes, elevation models, orthophotographs, three-dimensional models at different levels of details (LOD) and more. In addition, the regulations require that digital camera systems used for mapping purposes should be certified using a rigorous mapping systems certification and validation process which is specified in the Director General Instructions. The Director General Instructions for digital camera systems certification specify a two-step process as follows:

 1. Theoretical analysis of system components that includes: study of the accuracy of each component and an integrative error propagation evaluation, examination of the radiometric and spectral response curves for the imaging sensors, the calibration requirements, and the working procedures.

 2. Empirical study of the digital mapping system that examines a typical project (product scale, flight height, number and configuration of ground control points and process). The study examine all the aspects of the final product including; its accuracy, the product pixels size on the ground (spatial resolution), its completeness (missing pixels and striping affect), its radiometric properties (e.g., relative edge response) and its spectral characteristics (e.g., histogram spread, bands misalignment).

 This methodology was tested on a number of medium to large format digital cameras. The certification process is a basic stage in the mapping chain in Israel. This article provides the details of the Director General Instructions for digital camera systems certification, the methodology for certification and the tests that were carried out.
Absolute Radiometric Calibration of KOMPSAT-3A
Authors: Ahn H. Y. , Shin D. Y. , Kim J. S. , Seo D. C. , Choi C. U.
Published: August 2016

This paper presents a vicarious radiometric calibration of the Korea Multi-Purpose Satellite-3A (KOMPSAT-3A) performed by the Korea Aerospace Research Institute (KARI) and the Pukyong National University Remote Sensing Group (PKNU RSG) in 2015.The primary stages of this study are summarized as follows: (1) A field campaign to determine radiometric calibrated target fields was undertaken in Mongolia and South Korea. Surface reflectance data obtained in the campaign were input to a radiative transfer code that predicted at-sensor radiance. Through this process, equations and parameters were derived for the KOMPSAT-3A sensor to enable the conversion of calibrated DN to physical units, such as at-sensor radiance or TOA reflectance. (2) To validate the absolute calibration coefficients for the KOMPSAT-3A sensor, we performed a radiometric validation with a comparison of KOMPSAT-3A and Landsat-8 TOA reflectance using one of the six PICS (Libya 4). Correlations between top-of-atmosphere (TOA) radiances and the spectral band responses of the KOMPSAT-3A sensors at the Zuunmod, Mongolia and Goheung, South Korea sites were significant for multispectral bands. The average difference in TOA reflectance between KOMPSAT-3A and Landsat-8 image over the Libya 4, Libya site in the red-green-blue (RGB) region was under 3%, whereas in the NIR band, the TOA reflectance of KOMPSAT-3A was lower than the that of Landsat-8 due to the difference in the band passes of two sensors. The KOMPSAT-3Aensor includes a band pass near 940 nm that can be strongly absorbed by water vapor and therefore displayed low reflectance. Toovercome this, we need to undertake a detailed analysis using rescale methods, such as the spectral bandwidth adjustment factor.
A FRAMEWORK FOR AN AUTOMATIC SEAMLINE ENGINE
Authors: Al-Durgham M. , Downey M. , Gehrke S. , Beshah B. T.
Published: August 2016

Seamline generation is a crucial last step in the ortho-image mosaicking process. In particular, it is required to convolute residual geometric and radiometric imperfections that stem from various sources. In particular, temporal differences in the acquired data will cause the scene content and illumination conditions to vary. These variations can be modelled successfully. However, one is left with micro-differences that do need to be considered in seamline generation. Another cause of discrepancies originates from the rectification surface as it will not model the actual terrain and especially human-made objects perfectly. Quality of the image orientation will also contribute to the overall differences between adjacent ortho-rectified images.

 Our approach takes into consideration the aforementioned differences in designing a seamline engine. We have identified the following essential behaviours of the seamline in our engine: 1) Seamlines must pass through the path of least resistance, i.e., overlap areas with low radiometric differences. 2) Seamlines must not intersect with breaklines as that will lead to visible geometric artefacts. And finally, 3), shorter seamlines are generally favourable; they also result in faster operator review and, where necessary, interactive editing cycles. The engine design also permits alteration of the above rules for special cases.

 Although our preliminary experiments are geared towards line imaging systems (i.e., the Leica ADS family), our seamline engine remains sensor agnostic. Hence, our design is capable of mosaicking images from various sources with minimal effort. The main idea behind this engine is using graph cuts which, in spirit, is based of the max-flow min-cut theory. The main advantage of using graph cuts theory is that the generated solution is global in the energy minimization sense. In addition, graph cuts allows for a highly scalable design where a set of rules contribute towards a cost function which, in turn, influences the path of minimum resistance for the seamlines. In this paper, the authors present an approach for achieving quality seamlines relatively quickly and with emphasis on generating truly seamless ortho-mosaics.
IMPROVING SEMI-GLOBAL MATCHING: COST AGGREGATION AND CONFIDENCE MEASURE
Authors: d’Angelo Pablo
Published: August 2016

Digital elevation models are one of the basic products that can be generated from remotely sensed imagery. The Semi Global Matching (SGM) algorithm is a robust and practical algorithm for dense image matching. The connection between SGM and Belief Propagation was recently developed, and based on that improvements such as correction of over-counting the data term, and a new confidence measure have been proposed. Later the MGM algorithm has been proposed, it aims at improving the regularization step of SGM, but has only been evaluated on the Middlebury stereo benchmark so far. This paper evaluates these proposed improvements on the ISPRS satellite stereo benchmark, using a Pleiades Triplet and a Cartosat-1 Stereo pair. The over-counting correction slightly improves matching density, at the expense of adding a few outliers. The MGM cost aggregation shows leads to a slight increase of accuracy.
AUTOMATIC TEXTURE RECONSTRUCTION OF 3D CITY MODEL FROM OBLIQUE IMAGES
Authors: Kang Junhua , Deng Fei , Li Xinwei , Wan Fang
Published: August 2016

In recent years, the photorealistic 3D city models are increasingly important in various geospatial applications related to virtual city tourism, 3D GIS, urban planning, real-estate management. Besides the acquisition of high-precision 3D geometric data, texture reconstruction is also a crucial step for generating high-quality and visually realistic 3D models. However, most of the texture reconstruction approaches are probably leading to texture fragmentation and memory inefficiency. In this paper, we introduce an automatic framework of texture reconstruction to generate textures from oblique images for photorealistic visualization. Our approach include three major steps as follows: mesh parameterization, texture atlas generation and texture blending. Firstly, mesh parameterization procedure referring to mesh segmentation and mesh unfolding is performed to reduce geometric distortion in the process of mapping 2D texture to 3D model. Secondly, in the texture atlas generation step, the texture of each segmented region in texture domain is reconstructed from all visible images with exterior orientation and interior orientation parameters. Thirdly, to avoid color discontinuities at boundaries between texture regions, the final texture map is generated by blending texture maps from several corresponding images. We evaluated our texture reconstruction framework on a dataset of a city. The resulting mesh model can get textured by created texture without resampling. Experiment results show that our method can effectively mitigate the occurrence of texture fragmentation. It is demonstrated that the proposed framework is effective and useful for automatic texture reconstruction of 3D city model.
COMPENSATION LOW-FREQUENCY ERRORS IN TH-1 SATELLITE
Authors: Wang Jianrong , Wang Renxiang , Hu Xin
Published: August 2016

The topographic mapping products at 1:50,000 scale can be realized using satellite photogrammetry without ground control points (GCPs), which requires the high accuracy of exterior orientation elements. Usually, the attitudes of exterior orientation elements are obtained from the attitude determination system on the satellite. Based on the theoretical analysis and practice, the attitude determination system exists not only the high-frequency errors, but also the low-frequency errors related to the latitude of satellite orbit and the time. The low-frequency errors would affect the location accuracy without GCPs, especially to the horizontal accuracy. In SPOT5 satellite, the latitudinal model was proposed to correct attitudes using approximately 20 calibration sites data, and the location accuracy was improved. The low-frequency errors are also found in Tian Hui 1 (TH-1) satellite. Then, the method of compensation low-frequency errors is proposed in ground image processing of TH-1, which can detect and compensate the low-frequency errors automatically without using GCPs. This paper deal with the low-frequency errors in TH-1: First, the analysis about low-frequency errors of the attitude determination system is performed. Second, the compensation models are proposed in bundle adjustment. Finally, the verification is tested using data of TH-1. The testing results show: the low-frequency errors of attitude determination system can be compensated during bundle adjustment, which can improve the location accuracy without GCPs and has played an important role in the consistency of global location accuracy.