bluuebunny/crossref_metadata_2025 huggingface dataset:
SHORT-TERM PRECIPITATION OCCURRENCE PREDICTION FOR STRONG CONVECTIVE WEATHER USING FY2-G SATELLITE DATA: A CASE STUDY OF SHENZHEN,SOUTH CHINA
Authors: Chen Kai , Liu Jun , Guo Shanxin , Chen Jinsong , Liu Ping , Qian Jing , Chen Huijuan , Sun Bo
Published: August 2016

Short-term precipitation commonly occurs in south part of China, which brings intensive precipitation in local region for very short time. Massive water would cause the intensive flood inside of city when precipitation amount beyond the capacity of city drainage system. Thousands people’s life could be influenced by those short-term disasters and the higher city managements are required to facing these challenges. How to predict the occurrence of heavy precipitation accurately is one of the worthwhile scientific questions in meteorology. According to recent studies, the accuracy of short-term precipitation prediction based on numerical simulation model still remains low reliability, in some area where lack of local observations, the accuracy may be as low as 10%. The methodology for short term precipitation occurrence prediction still remains a challenge. In this paper, a machine learning method based on SVM was presented to predict short-term precipitation occurrence by using FY2-G satellite imagery and ground in situ observation data. The results were validated by traditional TS score which commonly used in evaluation of weather prediction. The results indicate that the proposed algorithm can present overall accuracy up to 90% for one-hour to six-hour forecast. The result implies the prediction accuracy could be improved by using machine learning method combining with satellite image. This prediction model can be further used to evaluated to predicted other characteristics of weather in Shenzhen in future.
INDOOR POSITIONING AND NAVIGATION BASED ON CONTROL SPHERECAL PANORAMIC IMAGES
Authors: Huang Tsung-Che , Tseng Yi-Hsing
Published: August 2016

Continuous indoor and outdoor positioning and navigation is the goal to achieve in the field of mobile mapping technology. However, accuracy of positioning and navigation will be largely degraded in indoor or occluded areas, due to receiving weak or less GNSS signals. Targeting the need of high accuracy indoor and outdoor positioning and navigation for mobile mapping applications, the objective of this study is to develop a novel method of indoor positioning and navigation with the use of spherical panoramic image (SPI). Two steps are planned in the technology roadmap. First, establishing a control SPI database that contains a good number of well-distributed control SPIs pre-acquired in the target space. A control SPI means an SPI with known exterior orientation parameters, which can be solved with a network bundle adjustment of SPIs. Having a control SPI database, the target space will be ready to provide the service of positioning and navigation. Secondly, the position and orientation of a newly taken SPI can be solved by using overlapped SPIs searched from the control SPI database. The method of matching SPIs and finding conjugate image features will be developed and tested. Two experiments will be planned and conducted in this paper to test the feasibility and validate the test results of the proposed methods. Analysis of appropriate number and distribution of needed control SPIs will also be included in the experiments with respect to different test cases.
SYNERGY OF OPTICAL AND SAR DATA FOR MAPPING AND MONITORING MANGROVES
Authors: Monzon A. K. , Reyes S. R. , Veridiano R. K. , Tumaneng R. , De Alban J. D.
Published: August 2016

Quantitative information on mangrove cover extents is essential in producing relevant resource management plans and conservation strategies. In the Philippines, mangrove rehabilitation was made a priority in relation to disaster risk response and mitigation following the calamities in the coastal communities during typhoon Haiyan/Yolanda; hence, baseline information on the extent of remaining mangrove cover was essential for effective site interventions. Although mangrove cover maps for the country already exists, analysis of mangrove cover changes were limited to the application of fixed annual deforestation rates due to the challenge of acquiring consistent temporal cloud-free optical satellite data over large landscapes. This study presents an initial analysis of SAR and optical imagery combined with field-based observations for detecting mangrove cover extent and changes through a straightforward graphical approach. The analysis is part of a larger study evaluating the synergistic use of time-series L-band SAR and optical data for mapping and monitoring of mangroves. Image segmentation was implemented on the 25-meter ALOS/PALSAR image mosaics, in which the generated objects were subjected to statistical analysis using the software R. In combination with selected Landsat bands, the class statistics from the image bands were used to generate decision trees and thresholds for the hierarchical image classification. The results were compared with global mangrove cover dataset and validated using collected ground truth data. This study developed an integrated replicable approach for analyzing future radar and optical datasets, essential in national level mangrove cover change monitoring and assessment for long-term conservation targets and strategies.
CONCEPT AND PRACTICE OF TEACHING TECHNICAL UNIVERSITY STUDENTS TO MODERN TECHNOLOGIES OF 3D DATA ACQUISITION AND PROCESSING: A CASE STUDY OF CLOSE-RANGE PHOTOGRAMMETRY AND TERRESTRIAL LASER SCANNING
Authors: Kravchenko Iulia , Luhmann Thomas , Shults Roman
Published: August 2016

For the preparation of modern specialists in the acquisition and processing of three-dimensional data, a broad and detailed study of related modern methods and technologies is necessary. One of the most progressive and effective methods of acquisition and analyzing spatial data is terrestrial laser scanning. The study of methods and technologies for terrestrial laser scanning is of great importance not only for GIS specialists, but also for surveying engineers who make decisions in traditional engineering tasks (monitoring, executive surveys, etc.). The understanding and formation of the right approach in preparing new professionals need to develop a modern and variable educational program. This educational program must provide effective practical and laboratory work and the student’s coursework. The resulting knowledge of the study should form the basis for practical or research of young engineers. In 2014, the Institute of Applied Sciences (Jade University Oldenburg, Germany) and Kyiv National University of Construction and Architecture (Kiev, Ukraine) had launched a joint educational project for the introduction of terrestrial laser scanning technology for collection and processing of spatial data. As a result of this project practical recommendations have been developed for the organization of educational processes in the use of terrestrial laser scanning. An advanced project-oriented educational program was developed which is presented in this paper. In order to demonstrate the effectiveness of the program a 3D model of the big and complex main campus of Kyiv National University of Construction and Architecture has been generated.
Spec Tool; an online education and research resource
Authors: Maman S. , Shenfeld A. , Isaacson S. , Blumberg D. G.
Published: August 2016

Education and public outreach (EPO) activities related to remote sensing, space, planetary and geo-physics sciences have been developed widely in the Earth and Planetary Image Facility (EPIF) at Ben-Gurion University of the Negev, Israel. These programs aim to motivate the learning of geo-scientific and technologic disciplines. For over the past decade, the facility hosts research and outreach activities for researchers, local community, school pupils, students and educators. As software and data are neither available nor affordable, the EPIF Spec tool was created as a web-based resource to assist in initial spectral analysis as a need for researchers and students. The tool is used both in the academic courses and in the outreach education programs and enables a better understanding of the theoretical data of spectroscopy and Imaging Spectroscopy in a 'hands-on' activity.

 This tool is available online and provides spectra visualization tools and basic analysis algorithms including Spectral plotting, Spectral angle mapping and Linear Unmixing. The tool enables to visualize spectral signatures from the USGS spectral library and additional spectra collected in the EPIF such as of dunes in southern Israel and from Turkmenistan. For researchers and educators, the tool allows loading collected samples locally for further analysis.
DETERMINING SPECTRAL REFLECTANCE COEFFICIENTS FROM HYPERSPECTRAL IMAGES OBTAINED FROM LOW ALTITUDES
Authors: Walczykowski P. , Jenerowicz A. , Orych A. , Siok K.
Published: August 2016

Remote Sensing plays very important role in many different study fields, like hydrology, crop management, environmental and ecosystem studies. For all mentioned areas of interest different remote sensing and image processing techniques, such as: image classification (object and pixel- based), object identification, change detection, etc. can be applied. Most of this techniques use spectral reflectance coefficients as the basis for the identification and distinction of different objects and materials, e.g. monitoring of vegetation stress, identification of water pollutants, yield identification, etc. Spectral characteristics are usually acquired using discrete methods such as spectrometric measurements in both laboratory and field conditions. Such measurements however can be very time consuming, which has led many international researchers to investigate the reliability and accuracy of using image-based methods. According to published and ongoing studies, in order to acquire these spectral characteristics from images, it is necessary to have hyperspectral data. The presented article describes a series of experiments conducted using the push-broom Headwall MicroHyperspec A-series VNIR. This hyperspectral scanner allows for registration of images with more than 300 spectral channels with a 1.9 nm spectral bandwidth in the 380- 1000 nm range. The aim of these experiments was to establish a methodology for acquiring spectral reflectance characteristics of different forms of land cover using such sensor. All research work was conducted in controlled conditions from low altitudes. Hyperspectral images obtained with this specific type of sensor requires a unique approach in terms of post-processing, especially radiometric correction. Large amounts of acquired imagery data allowed the authors to establish a new post- processing approach. The developed methodology allowed the authors to obtain spectral reflectance coefficients from a hyperspectral sensor mounted on an unmanned aerial vehicle, ensuring a high accuracy of obtained data.
COMPARING BROAD-BAND AND RED EDGE-BASED SPECTRAL VEGETATION INDICES TO ESTIMATE NITROGEN CONCENTRATION OF CROPS USING CASI DATA
Authors: Wang Yanjie , Liao Qinhong , Yang Guijun , Feng Haikuan , Yang Xiaodong , Yue Jibo
Published: August 2016

In recent decades, many spectral vegetation indices (SVIs) have been proposed to estimate the leaf nitrogen concentration (LNC) of crops. However, most of these indices were based on the field hyperspectral reflectance. To test whether they can be used in aerial remote platform effectively, in this work a comparison of the sensitivity between several broad-band and red edge-based SVIs to LNC is investigated over different crop types. By using data from experimental LNC values over 4 different crop types and image data acquired using the Compact Airborne Spectrographic Imager (CASI) sensor, the extensive dataset allowed us to evaluate broad-band and red edge-based SVIs. The result indicated that NDVI performed the best among the selected SVIs while red edge-based SVIs didn’t show the potential for estimating the LNC based on the CASI data due to the spectral resolution. In order to search for the optimal SVIs, the band combination algorithm has been used in this work. The best linear correlation against the experimental LNC dataset was obtained by combining the 626.20nm and 569.00nm wavebands. These wavelengths correspond to the maximal chlorophyll absorption and reflection position region, respectively, and are known to be sensitive to the physiological status of the plant. Then this linear relationship was applied to the CASI image for generating an LNC map, which can guide farmers in the accurate application of their N fertilization strategies.
MONITORING OF THE RIVERBEDS OF RIVERS DNIESTER AND TISZA OF THE CARPATHIAN REGION
Authors: Burshtynska K. , Shevchuk V. , Tretyak S. , Vekliuk V.
Published: August 2016

The paper analyses the causes of riverbed shifts of the rivers Dniester and Tisza of the Carpathian region. Among these reasons are mostly climate, physical and geographical, and anthropogenic factors, including frequent floods, weak rocks and soils, logging, and removal of gravel and sand materials out of the channels. The research considers the transition of the Dniester and Tisza rivers from mountaineous area into the lowland. The analysis of these shifts has been carried out on the 100 km long sections over a 100-year period. We used topographic maps and space images, as well as special geological and soil maps, as materials for this research. We have determined that the shifts of the Dniester and Tisza riverbeds can reach up to 500-1000 m in some sections. We also analyze the risks and dangers that follow such shifts.
EFFECT OF PANSHARPENED IMAGE ON SOME OF PIXEL BASED AND OBJECT BASED CLASSIFICATION ACCURACY
Authors: Karakus P. , Karabork H.
Published: August 2016

Classification is the most important method to determine type of crop contained in a region for agricultural planning. There are two types of the classification. First is pixel based and the other is object based classification method. While pixel based classification methods are based on the information in each pixel, object based classification method is based on objects or image objects that formed by the combination of information from a set of similar pixels. Multispectral image contains a higher degree of spectral resolution than a panchromatic image. Panchromatic image have a higher spatial resolution than a multispectral image. Pan sharpening is a process of merging high spatial resolution panchromatic and high spectral resolution multispectral imagery to create a single high resolution color image. The aim of the study was to compare the potential classification accuracy provided by pan sharpened image. In this study, SPOT 5 image was used dated April 2013. 5m panchromatic image and 10m multispectral image are pan sharpened. Four different classification methods were investigated: maximum likelihood, decision tree, support vector machine at the pixel level and object based classification methods. SPOT 5 pan sharpened image was used to classification sun flowers and corn in a study site located at Kadirli region on Osmaniye in Turkey. The effects of pan sharpened image on classification results were also examined. Accuracy assessment showed that the object based classification resulted in the better overall accuracy values than the others. The results that indicate that these classification methods can be used for identifying sun flower and corn and estimating crop areas.
A REGION-BASED MULTI-SCALE APPROACH FOR OBJECT-BASED IMAGE ANALYSIS
Authors: Kavzoglu T. , Yildiz Erdemir M. , Tonbul H.
Published: August 2016

Within the last two decades, object-based image analysis (OBIA) considering objects (i.e. groups of pixels) instead of pixels has gained popularity and attracted increasing interest. The most important stage of the OBIA is image segmentation that groups spectrally similar adjacent pixels considering not only the spectral features but also spatial and textural features. Although there are several parameters (scale, shape, compactness and band weights) to be set by the analyst, scale parameter stands out the most important parameter in segmentation process. Estimating optimal scale parameter is crucially important to increase the classification accuracy that depends on image resolution, image object size and characteristics of the study area. In this study, two scale-selection strategies were implemented in the image segmentation process using pan-sharped Qickbird-2 image. The first strategy estimates optimal scale parameters for the eight sub-regions. For this purpose, the local variance/rate of change (LV-RoC) graphs produced by the ESP-2 tool were analysed to determine fine, moderate and coarse scales for each region. In the second strategy, the image was segmented using the three candidate scale values (fine, moderate, coarse) determined from the LV-RoC graph calculated for whole image. The nearest neighbour classifier was applied in all segmentation experiments and equal number of pixels was randomly selected to calculate accuracy metrics (overall accuracy and kappa coefficient). Comparison of region-based and image-based segmentation was carried out on the classified images and found that region-based multi-scale OBIA produced significantly more accurate results than image-based single-scale OBIA. The difference in classification accuracy reached to 10% in terms of overall accuracy.
REGION OF INTEREST DETECTION BASED ON HISTOGRAM SEGMENTATION FOR SATELLITE IMAGE
Authors: Kiadtikornthaweeyot Warinthorn , Tatnall Adrian R. L.
Published: August 2016

High resolution satellite imaging is considered as the outstanding applicant to extract the Earth’s surface information. Extraction of a feature of an image is very difficult due to having to find the appropriate image segmentation techniques and combine different methods to detect the Region of Interest (ROI) most effectively. This paper proposes techniques to classify objects in the satellite image by using image processing methods on high-resolution satellite images. The systems to identify the ROI focus on forests, urban and agriculture areas. The proposed system is based on histograms of the image to classify objects using thresholding. The thresholding is performed by considering the behaviour of the histogram mapping to a particular region in the satellite image. The proposed model is based on histogram segmentation and morphology techniques. There are five main steps supporting each other; Histogram classification, Histogram segmentation, Morphological dilation, Morphological fill image area and holes and ROI management. The methods to detect the ROI of the satellite images based on histogram classification have been studied, implemented and tested. The algorithm is be able to detect the area of forests, urban and agriculture separately. The image segmentation methods can detect the ROI and reduce the size of the original image by discarding the unnecessary parts.
AUTOMATIC APPROACH TO VHR SATELLITE IMAGE CLASSIFICATION
Authors: Kupidura P. , Osińska-Skotak K. , Pluto-Kossakowska J.
Published: August 2016

In this paper, we present a proposition of a fully automatic classification of VHR satellite images. Unlike the most widespread approaches: supervised classification, which requires prior defining of class signatures, or unsupervised classification, which must be followed by an interpretation of its results, the proposed method requires no human intervention except for the setting of the initial parameters. The presented approach bases on both spectral and textural analysis of the image and consists of 3 steps. The first step, the analysis of spectral data, relies on NDVI values. Its purpose is to distinguish between basic classes, such as water, vegetation and non-vegetation, which all differ significantly spectrally, thus they can be easily extracted basing on spectral analysis. The second step relies on granulometric maps. These are the product of local granulometric analysis of an image and present information on the texture of each pixel neighbourhood, depending on the texture grain. The purpose of texture analysis is to distinguish between different classes, spectrally similar, but yet of different texture, e.g. bare soil from a built-up area, or low vegetation from a wooded area. Due to the use of granulometric analysis, based on mathematical morphology opening and closing, the results are resistant to the border effect (qualifying borders of objects in an image as spaces of high texture), which affect other methods of texture analysis like GLCM statistics or fractal analysis. Therefore, the effectiveness of the analysis is relatively high. Several indices based on values of different granulometric maps have been developed to simplify the extraction of classes of different texture. The third and final step of the process relies on a vegetation index, based on near infrared and blue bands. Its purpose is to correct partially misclassified pixels. All the indices used in the classification model developed relate to reflectance values, so the preliminary step of recalculation of pixel DNs to reflectance is required. Thanks to this, the proposed approach is in theory universal, and might be applied to different satellite system images of different acquisition dates. The test data consists of 3 Pleiades images captured on different dates. Research allowed to determine optimal indices values. Using the same parameters, we obtained a very good accuracy of extraction of 5 land cover/use classes: water, low vegetation, bare soil, wooded area and built-up area in all the test images (kappa from 87% to 96%). What constitutes important, even significant changes in parameter values, did not cause a significant declination of classification accuracy, which demonstrates how robust the proposed method is.
CLOUD DETECTION OF OPTICAL SATELLITE IMAGES USING SUPPORT VECTOR MACHINE
Authors: Lee Kuan-Yi , Lin Chao-Hung
Published: August 2016

Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM) is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study.

 The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA) algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012) uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate the detection accuracy of the proposed method is better than related methods.
AUTOMATIC SEA BIRD DETECTION FROM HIGH RESOLUTION AERIAL IMAGERY
Authors: Mader S. , Grenzdörffer G. J.
Published: August 2016

Great efforts are presently taken in the scientific community to develop computerized and (fully) automated image processing methods allowing for an efficient and automatic monitoring of sea birds and marine mammals in ever-growing amounts of aerial imagery. Currently the major part of the processing, however, is still conducted by especially trained professionals, visually examining the images and detecting and classifying the requested subjects. This is a very tedious task, particularly when the rate of void images regularly exceeds the mark of 90%. In the content of this contribution we will present our work aiming to support the processing of aerial images by modern methods from the field of image processing. We will especially focus on the combination of local, region-based feature detection and piecewise global image segmentation for automatic detection of different sea bird species. Large image dimensions resulting from the use of medium and large-format digital cameras in aerial surveys inhibit the applicability of image processing methods based on global operations. In order to efficiently handle those image sizes and to nevertheless take advantage of globally operating segmentation algorithms, we will describe the combined usage of a simple performant feature detector based on local operations on the original image with a complex global segmentation algorithm operating on extracted sub-images. The resulting exact segmentation of possible candidates then serves as a basis for the determination of feature vectors for subsequent elimination of false candidates and for classification tasks.
ESTIMATING WOOD VOLUME FOR PINUS BRUTIA TREES IN FOREST STANDS FROM QUICKBIRD-2 IMAGERY
Authors: Patias Petros , Stournara Panagiota
Published: August 2016

Knowledge of forest parameters, such as wood volume, is required for a sustainable forest management. Collecting such information in the field is laborious and even not feasible in inaccessible areas. In this study, tree wood volume is estimated utilizing remote sensing techniques, which can facilitate the extraction of relevant information. The study area is the University Forest of Taxiarchis, which is located in central Chalkidiki, Northern Greece and covers an area of 58km2. The tree species under study is the conifer evergreen species P. brutia (Calabrian pine). Three plot surfaces of 10m radius were used. VHR Quickbird-2 images are used in combination with an allometric relationship connecting the Tree Crown with the Diameter at breast height (Dbh), and a volume table developed for Greece. The overall methodology is based on individual tree crown delineation, based on (a) the marker-controlled watershed segmentation approach and (b) the GEographic Object-Based Image Analysis approach. The aim of the first approach is to extract separate segments each of them including a single tree and eventual lower vegetation, shadows, etc. The aim of the second approach is to detect and remove the “noisy” background. In the application of the first approach, the Blue, Green, Red, Infrared and PCA-1 bands are tested separately. In the application of the second approach, NDVI and image brightness thresholds are utilized. The achieved results are evaluated against field plot data. Their observed difference are between -5% to +10%.
POLARIMETRIC SAR DATA GMM CLASSIFICATION BASED ON IMPROVED FREEMAN INCOHERENT DECOMPOSITION
Authors: Rouabah S. , Ouarzeddine M. , Azmedroub B.
Published: August 2016

Due to the increasing volume of available SAR Data, powerful classification processings are needed to interpret the images. GMM (Gaussian Mixture Model) is widely used to model distributions. In most applications, GMM algorithm is directly applied on raw SAR data, its disadvantage is that forest and urban areas are classified with the same label and gives problems in interpretation. In this paper, a combination between the improved Freeman decomposition and GMM classification is proposed. The improved Freeman decomposition powers are used as feature vectors for GMM classification. The E-SAR polarimetric image acquired over Oberpfaffenhofen in Germany is used as data set. The result shows that the proposed combination can solve the standard GMM classification problem.
PRELIMINARY RESULTS OF EARTHQUAKE-INDUCED BUILDING DAMAGE DETECTION WITH OBJECT-BASED IMAGE CLASSIFICATION
Authors: Sabuncu A. , Uca Avci Z. D. , Sunar F.
Published: August 2016

Earthquakes are the most destructive natural disasters, which result in massive loss of life, infrastructure damages and financial losses. Earthquake-induced building damage detection is a very important step after earthquakes since earthquake-induced building damage is one of the most critical threats to cities and countries in terms of the area of damage, rate of collapsed buildings, the damage grade near the epicenters and also building damage types for all constructions. Van-Ercis (Turkey) earthquake (Mw= 7.1) was occurred on October 23th, 2011; at 10:41 UTC (13:41 local time) centered at 38.75 N 43.36 E that places the epicenter about 30 kilometers northern part of the city of Van. It is recorded that, 604 people died and approximately 4000 buildings collapsed or seriously damaged by the earthquake.

 In this study, high-resolution satellite images of Van-Ercis, acquired by Quickbird-2 (© Digital Globe Inc.) after the earthquake, were used to detect the debris areas using an object-based image classification. Two different land surfaces, having homogeneous and heterogeneous land covers, were selected as case study areas. As a first step of the object-based image processing, segmentation was applied with a convenient scale parameter and homogeneity criterion parameters. As a next step, condition based classification was used. In the final step of this preliminary study, outputs were compared with streetview/ortophotos for the verification and evaluation of the classification accuracy.
ACCOUNTING FOR VARIANCE IN HYPERSPECTRAL DATA COMING FROM LIMITATIONS OF THE IMAGING SYSTEM
Authors: Shurygin B. , Shestakova M. , Nikolenko A. , Badasen E. , Strakhov P.
Published: August 2016

Over the course of the past few years, a number of methods was developed to incorporate hyperspectral imaging specifics into generic data mining techniques, traditionally used for hyperspectral data processing. Projection pursuit methods embody the largest class of methods empoyed for hyperspectral image data reduction, however, they all have certain drawbacks making them either hard to use or inefficient. It has been shown that hyperspectral image (HSI) statistics tend to display “heavy tails” (Manolakis2003)(Theiler2005), rendering most of the projection pursuit methods hard to use. Taking into consideration the magnitude of described deviations of observed data PDFs from normal distribution, it is apparent that a priori knowledge of variance in data caused by the imaging system is to be employed in order to efficiently classify objects on HSIs (Kerr, 2015), especially in cases of wildly varying SNR. A number of attempts to describe this variance and compensating techniques has been made (Aiazzi2006), however, new data quality standards are not yet set and accounting for the detector response is made under large set of assumptions. Current paper addresses the issue of hyperspectral image classification in the context of different variance sources based on the knowledge of calibration curves (both spectral and radiometric) obtained for each pixel of imaging camera. A camera produced by ZAO NPO Lepton (Russia) was calibrated and used to obtain a test image. A priori known values of SNR and spectral channel cross-correlation were incorporated into calculating test statistics used in dimensionality reduction and feature extraction. Expectation-Maximization classification algorithm modification for non-Gaussian model as described by (Veracini2010) was further employed. The impact of calibration data coarsening by ignoring non-uniformities on false alarm rate was studied. Case study shows both regions of scene-dominated variance and sensor-dominated variance, leading to different preprocession parameters and, ultimatively, classification results. A multilevel system for denoting hyperspectral pushbroom scanners calibration quality was proposed.
A FUZZY LOGIC-BASED APPROACH FOR THE DETECTION OF FLOODED VEGETATION BY MEANS OF SYNTHETIC APERTURE RADAR DATA
Authors: Tsyganskaya V. , Martinis S. , Twele A. , Cao W. , Schmitt A. , Marzahn P. , Ludwig R.
Published: August 2016

In this paper an algorithm designed to map flooded vegetation from synthetic aperture radar (SAR) imagery is introduced. The approach is based on fuzzy logic which enables to deal with the ambiguity of SAR data and to integrate multiple ancillary data containing topographical information, simple hydraulic considerations and land cover information. This allows the exclusion of image elements with a backscatter value similar to flooded vegetation, to significantly reduce misclassification errors. The flooded vegetation mapping procedure is tested on a flood event that occurred in Germany over parts of the Saale catchment on January 2011 using a time series of high resolution TerraSAR-X data covering the time interval from 2009 to 2015. The results show that the analysis of multi-temporal X-band data combined with ancillary data using a fuzzy logic-based approach permits the detection of flooded vegetation areas.
BASELINE ESTIMATION ALGORITHM WITH BLOCK ADJUSTMENT FOR MULTI-PASS DUAL-ANTENNA INSAR
Authors: Jin Guowang , Xiong Xin , Xu Qing , Gong Zhihui , Zhou Yang
Published: August 2016

Baseline parameters and interferometric phase offset need to be estimated accurately, for they are key parameters in processing of InSAR (Interferometric Synthetic Aperture Radar). If adopting baseline estimation algorithm with single pass, it needs large quantities of ground control points to estimate interferometric parameters for mosaicking multiple passes dual-antenna airborne InSAR data that covers large areas. What’s more, there will be great difference between heights derived from different passes due to the errors of estimated parameters. So, an estimation algorithm of interferometric parameters with block adjustment for multi-pass dual-antenna InSAR is presented to reduce the needed ground control points and height’s difference between different passes. The baseline estimation experiments were done with multi-pass InSAR data obtained by Chinese dual-antenna airborne InSAR system. Although there were less ground control points, the satisfied results were obtained, as validated the proposed baseline estimation algorithm.
A DATA FIELD METHOD FOR URBAN REMOTELY SENSED IMAGERY CLASSIFICATION CONSIDERING SPATIAL CORRELATION
Authors: Zhang Y. , Qin K. , Zeng C. , Zhang E. B. , Yue M. X. , Tong X.
Published: August 2016

Spatial correlation between pixels is important information for remotely sensed imagery classification. Data field method and spatial autocorrelation statistics have been utilized to describe and model spatial information of local pixels. The original data field method can represent the spatial interactions of neighbourhood pixels effectively. However, its focus on measuring the grey level change between the central pixel and the neighbourhood pixels results in exaggerating the contribution of the central pixel to the whole local window. Besides, Geary’s C has also been proven to well characterise and qualify the spatial correlation between each pixel and its neighbourhood pixels. But the extracted object is badly delineated with the distracting salt-and-pepper effect of isolated misclassified pixels. To correct this defect, we introduce the data field method for filtering and noise limitation. Moreover, the original data field method is enhanced by considering each pixel in the window as the central pixel to compute statistical characteristics between it and its neighbourhood pixels. The last step employs a support vector machine (SVM) for the classification of multi-features (e.g. the spectral feature and spatial correlation feature). In order to validate the effectiveness of the developed method, experiments are conducted on different remotely sensed images containing multiple complex object classes inside. The results show that the developed method outperforms the traditional method in terms of classification accuracies.
NATIONWIDE HYBRID CHANGE DETECTION OF BUILDINGS
Authors: Hron V. , Halounova L.
Published: August 2016

The Fundamental Base of Geographic Data of the Czech Republic (hereinafter FBGD) is a national 2D geodatabase at a 1:10,000 scale with more than 100 geographic objects. This paper describes the design of the permanent updating mechanism of buildings in FBGD. The proposed procedure belongs to the category of hybrid change detection (HCD) techniques which combine pixel-based and object-based evaluation. The main sources of information for HCD are cadastral information and bi-temporal vertical digital aerial photographs. These photographs have great information potential because they contain multispectral, position and also elevation information. Elevation information represents a digital surface model (DSM) which can be obtained using the image matching technique. Pixel-based evaluation of bi-temporal DSMs enables fast localization of places with potential building changes. These coarse results are subsequently classified through the object-based image analysis (OBIA) using spectral, textural and contextual features and GIS tools. The advantage of the two-stage evaluation is the pre-selection of locations where image segmentation (a computationally demanding part of OBIA) is performed. It is not necessary to apply image segmentation to the entire scene, but only to the surroundings of detected changes, which contributes to significantly faster processing and lower hardware requirements. The created technology is based on open-source software solutions that allow easy portability on multiple computers and parallelization of processing. This leads to significant savings of financial resources which can be expended on the further development of FBGD.
MONITORING OF FLUVIAL TRANSPORT IN THE MOUNTAIN RIVER BED USING TERRESTRIAL LASER SCANNING
Authors: Jozkow G. , Borkowski A. , Kasprzak M.
Published: August 2016

The fluvial transport is the surface process that has a strong impact on the topography changes, especially in mountain areas. Traditional hydrological measurements usually give a good understanding of the river flow, however, the information of the bedload movement in the rivers is still insufficient. In particular, there is limited knowledge about the movement of the largest clasts, i.e. boulders. This investigation addresses mentioned issues by employing Terrestrial Laser Scanning (TLS) to monitor annual changes of the mountain river bed. The vertical changes were estimated based on the Digital Elevation Model (DEM) of difference (DoD) while transported boulders were identified based on the distances between point clouds and RGB-coloured points. Combined RGB point clouds allowed also to measure 3D displacements of boulders. The results showed that the highest dynamic of the fluvial process occurred between years 2012-2013. Obtained DoD clearly indicated alternating zones of erosion and deposition of the sediment finer fractions in the local sedimentary traps. The horizontal displacement of the rock material in the river bed showed high complexity resulting in the displacement of large boulders (major axis about 0.8 m) for the distance up to 2.3 m.
LAND COVER CHANGE DETECTION USING SALIENCY ANDWAVELET TRANSFORMATION
Authors: Zhang Haopeng , Jiang Zhiguo , Cheng Yan
Published: August 2016

How to obtain accurate difference map remains an open challenge in change detection. To tackle this problem, we propose a change detection method based on saliency detection and wavelet transformation. We do frequency-tuned saliency detection in initial difference image (IDI) obtained by logarithm ratio to get a salient difference image (SDI). Then, we calculate local entropy of SDI to obtain an entropic salient difference image (ESDI). The final difference image (FDI) is the wavelet fusion of IDI and ESDI, and Otsu thresholding is used to extract difference map from FDI. Experimental results validate the effectiveness and feasibility.
INTERGRATION OF LiDAR DATA WITH AERIAL IMAGERY FOR ESTIMATING ROOFTOP SOLAR PHOTOVOLTAIC POTENTIALS IN CITY OF CAPE TOWN
Authors: Adeleke A. K. , Smit J. L.
Published: August 2016

Apart from the drive to reduce carbon dioxide emissions by carbon-intensive economies like South Africa, the recent spate of electricity load shedding across most part of the country, including Cape Town has left electricity consumers scampering for alternatives, so as to rely less on the national grid. Solar energy, which is adequately available in most part of Africa and regarded as a clean and renewable source of energy, makes it possible to generate electricity by using photovoltaics technology. However, before time and financial resources are invested into rooftop solar photovoltaic systems in urban areas, it is important to evaluate the potential of the building rooftop, intended to be used in harvesting the solar energy. This paper presents methodologies making use of LiDAR data and other ancillary data, such as high-resolution aerial imagery, to automatically extract building rooftops in City of Cape Town and evaluate their potentials for solar photovoltaics systems. Two main processes were involved: (1) automatic extraction of building roofs using the integration of LiDAR data and aerial imagery in order to derive its’ outline and areal coverage; and (2) estimating the global solar radiation incidence on each roof surface using an elevation model derived from the LiDAR data, in order to evaluate its solar photovoltaic potential. This resulted in a geodatabase, which can be queried to retrieve salient information about the viability of a particular building roof for solar photovoltaic installation.
IR SPECTRAL MAPPING OF THE MARTIAN SOUTH POLAR RESIDUAL CAP USING CRISM
Authors: Campbell Jacqueline , Sidiropoulos Panagiotis , Muller Jan-Peter
Published: August 2016

Polycyclic aromatic hydrocarbons (PAHs) are considered to be important in theories of abiogenesis (Allamandola, 2011) . There is evidence that PAHs have been detected on two icy Saturnian satellites using the Visual and Infrared Mapping Spectrometer (VIMS) on the Cassini spacecraft (Cruikshank et al., 2007). The hypothesised presence of PAHs in Mars south polar cap has not been systematically examined even though the Mars south polar cap may allow the preservation of organic molecules that are typically destroyed at the Martian surface by UV radiation (Dartnell et al. 2012). This hypothesis is supported by recent analyses of South Polar Residual Cap (SPRC) structural evolution (Thomas et al., 2009) that suggest the possibility that seasonal and long term sublimation may excavate dust particles from within the polar ice. Periodic sublimation is believed to be responsible for the formation of so-called “Swiss Cheese Terrain”, a unique surface feature found only in the Martian south polar residual cap consisting of flat floored, circular depressions (Byrne, 2009). We show the first examples of work towards the detection of PAHs in Swiss Cheese Terrain, using data from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM), on board NASA’s Mars Reconnaissance Orbiter (MRO). CRISM is designed to search for mineralogical indications of past and present water, thus providing extensive coverage of the south polar cap. In this work, we discuss whether CRISM infrared spectra can be used to detect PAHs in Swiss Cheese Terrain and demonstrate a number of maps showing shifts in spectral profiles over the SPRC.
MERGING DIGITAL SURFACE MODELS IMPLEMENTING BAYESIAN APPROACHES
Authors: Sadeq H. , Drummond J. , Li Z.
Published: August 2016

In this research different DSMs from different sources have been merged. The merging is based on a probabilistic model using a Bayesian Approach. The implemented data have been sourced from very high resolution satellite imagery sensors (e.g. WorldView-1 and Pleiades). It is deemed preferable to use a Bayesian Approach when the data obtained from the sensors are limited and it is difficult to obtain many measurements or it would be very costly, thus the problem of the lack of data can be solved by introducing a priori estimations of data. To infer the prior data, it is assumed that the roofs of the buildings are specified as smooth, and for that purpose local entropy has been implemented. In addition to the a priori estimations, GNSS RTK measurements have been collected in the field which are used as check points to assess the quality of the DSMs and to validate the merging result. The model has been applied in the West-End of Glasgow containing different kinds of buildings, such as flat roofed and hipped roofed buildings. Both quantitative and qualitative methods have been employed to validate the merged DSM. The validation results have shown that the model was successfully able to improve the quality of the DSMs and improving some characteristics such as the roof surfaces, which consequently led to better representations. In addition to that, the developed model has been compared with the well established Maximum Likelihood model and showed similar quantitative statistical results and better qualitative results. Although the proposed model has been applied on DSMs that were derived from satellite imagery, it can be applied to any other sourced DSMs.
EXTRACTION OF COASTLINES WITH FUZZY APPROACH USING SENTINEL-1 SAR IMAGE
Authors: Demir N. , Kaynarca M. , Oy S.
Published: August 2016

Coastlines are important features for water resources, sea products, energy resources etc. Coastlines are changed dynamically, thus automated methods are necessary for analysing and detecting the changes along the coastlines. In this study, Sentinel-1 C band SAR image has been used to extract the coastline with fuzzy logic approach. The used SAR image has VH polarisation and 10x10m. spatial resolution, covers 57 sqkm area from the south-east of Puerto-Rico. Additionally, radiometric calibration is applied to reduce atmospheric and orbit error, and speckle filter is used to reduce the noise. Then the image is terrain-corrected using SRTM digital surface model. Classification of SAR image is a challenging task since SAR and optical sensors have very different properties. Even between different bands of the SAR sensors, the images look very different. So, the classification of SAR image is difficult with the traditional unsupervised methods. In this study, a fuzzy approach has been applied to distinguish the coastal pixels than the land surface pixels. The standard deviation and the mean, median values are calculated to use as parameters in fuzzy approach. The Mean-standard-deviation (MS) Large membership function is used because the large amounts of land and ocean pixels dominate the SAR image with large mean and standard deviation values. The pixel values are multiplied with 1000 to easify the calculations. The mean is calculated as 23 and the standard deviation is calculated as 12 for the whole image. The multiplier parameters are selected as a: 0.58, b: 0.05 to maximize the land surface membership. The result is evaluated using airborne LIDAR data, only for the areas where LIDAR dataset is available and secondly manually digitized coastline. The laser points which are below 0,5 m are classified as the ocean points. The 3D alpha-shapes algorithm is used to detect the coastline points from LIDAR data. Minimum distances are calculated between the LIDAR points of coastline with the extracted coastline. The statistics of the distances are calculated as following; the mean is 5.82m, standard deviation is 5.83m and the median value is 4.08 m. Secondly, the extracted coastline is also evaluated with manually created lines on SAR image. Both lines are converted to dense points with 1 m interval. Then the closest distances are calculated between the points from extracted coastline and manually created coastline. The mean is 5.23m, standard deviation is 4.52m. and the median value is 4.13m for the calculated distances. The evaluation values are within the accuracy of used SAR data for both quality assessment approaches.