bluuebunny/crossref_metadata_2025 huggingface dataset:
AN IMPROVED ALGORITHM USED IN AUTOMATIC MATCHING FOR LOW-ALTITUDE AERIAL IMAGE
Authors: Wan X. , Zhang Z. , Wan Y.
Published: August 2012

Abstract. In this paper, we present an improved algorithm used for low altitude aerial image automatic matching based on SIFT operator. Compared to traditional photogrammetry based on platforms such as satellites and aerospace aircrafts, the platforms of low-altitude remote sensing system have relatively lighter weight, therefore existing rotation angle and scale differences in the stereo-images. In addition, there appears fracture lines and the discontinuities of parallax in the elevation undulating area. The characteristics above make it unsuitable for the traditional photogrammetry matching method based on grey scale correlation and the matching search strategy based on continuous parallax. In this paper an improved SIFT(Scale-invariant feature transform) operator is applied to the automatic matching of low-altitude aerial images. Several improvements were made to enhance the feature recurrence rate, matching correct rate and speed of matching. Firstly, we applied the theory of zero-crossing in SIFT feature extraction introducing the image geometry feature in scale space detection. Secondly, correlation coefficient is used as similarity measure instead of Euclidean distance in SIFT algorithm. Thirdly, we proposed a new matching strategy based on principal orientation constrain to shorten the search distance compared to the global search in SIFT algorithm. To demonstrate the feasibility of the approach, experiments were carried with four groups low-altitude remote sensing stereo-images from different sensors, and presented different distortions. Results showed that the improved algorithm has higher feature recurrence rate, matching correct rate and speed of matching dealing with different scale, large rotation angle, affine distortion and nonlinear distortion of low-altitude remote sensing stereo-images.
INTERNATIONAL SUMMER STUDENT SEMINAR (3S), FROM IDEA TO REALIZATION
Authors: Karpik A. P. , Peled A. , Musikhin L. A. , Deren L. , Seredovich V. A.
Published: August 2012

Abstract. The article gives a spotlight on a three universities (Moscow State University of Geodesy and Cartography, Siberian State Academy of Geodesy, Wuhan University) and ISPRS’ cooperation in organizing and holding summer student seminars for undergraduate, master, and PhD students of the universities specializing in photogrammetry, remote sensing, GIS, aerial and space cartography. Principles of topics of the seminar selection, lecture courses delivering and practical training as well as international joint team work on projects and their defence are described. The description of the basic stages of 3S implementation, and the goals of this sort of the international event is given.
SATELLITE BASED EDUCATION AND TRAINING IN REMOTE SENSING AND GEO-INFORMATION: AN E-LEARNING APPROACH TO MEET THE GROWING DEMANDS IN INDIA
Authors: Raju P. L. N. , Gupta P. K.
Published: August 2012

Abstract. One of the prime activities of Indian Space Research Organisation's (ISRO) Space Program is providing satellite communication services, viz., television broadcasting, mobile communication, cyclone disaster warning and rescue operations etc. so as to improve their economic conditions, disseminate technical / scientific knowledge to improve the agriculture production and education for rural people of India. ISRO, along with National Aeronautical and Space Administration (NASA) conducted experimental satellite communication project i.e. Satellite Instructional Television Experiment (SITE) using NASA’s Advanced Telecommunication Satellite (i.e. ATS 6) with an objective to educate poor people of India via satellite broadcasting in 1975 and 1976, covering more than 2600 villages in six states of India and territories. Over the years India built communication satellites indigenously to meet the communication requirements of India. This has further lead to launch of an exclusive satellite from ISRO for educational purposes i.e. EDUSAT in 2004 through which rich audio-video content is transmitted / received, recreating virtual classes through interactivity. Indian Institute of Remote Sensing (IIRS) established in 1966, a premier institute in south East Asia in disseminating Remote Sensing (RS) and Geographical Information System (GIS), mainly focusing on contact based programs. But expanded the scope with satellite based Distance Learning Programs for Universities, utilizing the dedicated communication satellite i.e. EDUSAT in 2007. IIRS conducted successfully eight Distance Learning Programs in the last five years and training more than 6000 students mainly at postgraduate level from more than 60 universities /Institutions spread across India. IIRS obtained feedback and improved the programs on the continuous basis. Expanded the scope of IIRS outreach program to train user departments tailor made in any of the applications of Remote Sensing and Geoinformation, capacity building for ISRO’s operational projects / new satellite missions, developing e-learning contents and launching e-learning courses under twelfth five year (i.e. 2012–17) plan period of Government of India, in addition to continuing of existing distance learning programs for universities.
ISSUES AND CHALLENGES IN GEOMATICS EDUCATION IN HARYANA STATE, INDIA
Authors: Chaudhary B. S.
Published: August 2012

Abstract. In Haryana, the beginning of Geomatics may be traced back to the establishment of Haryana State Remote Sensing Application Centre (HARSAC) now renamed as Haryana Space Application Centre at Hisar under the aegis of Department of Science and Technology, Govt. of Haryana with the active support from Department of Space, Govt. of India in 1986. Though, Gurgaon has developed as Geomatics hub housing many national and international collaborators but mostly involved in RS& GIS applications and rarely as a training centre. Looking to the needs of the trained manpower in Geomatics, PG diploma course in remote sensing and GIS was started by MDU, Rohtak in 2005 followed by CRM, Jat College Hisar in 2007. GJU, Hisar came up with M. Tech. degree in Geoinformatics in collaboration with HARSAC, Hisar two years before i.e. in 2009. MDU, Rohtak has also come up the M. Tech. programme in Geoinformatics this year. The Geo-sciences disciplines including the department of Geophysics, Geology, Geography and Environmental Sciences in different universities in Haryana are also having a compulsory paper on RS, GIS and GPS emphasizing more on the fundamentals and applications specifically in their specific domains. The Department of Geophysics, Kurukshetra University, Kurukshetra has also established Geoinformatics lab with funding from UGC to cater the training needs in this sector. The present paper discusses critical issues related to the present status, grey areas and future scenario of Geomatics education in Haryana.
APPLIED GEOSPATIAL EDUCATION: ACQUISITION AND PROCESSING OF HIGH RESOLUTION AIRBORNE LIDAR AND ORTHOIMAGES FOR THE GREAT SMOKY MOUNTAINS NATIONAL PARK, SOUTHEASTERN UNITED STATES
Authors: Jordan T. R. , Madden M. , Sharma J. B. , Panda S. S.
Published: August 2012

Abstract. In an innovative collaboration between government, university and private industry, researchers at the University of Georgia and Gainesville State College are collaborating with Photo Science, Inc. to acquire, process and quality control check lidar and or-thoimages of forest areas in the Southern Appalachian Mountains of the United States. Funded by the U.S. Geological Survey, this project meets the objectives of the ARRA initiative by creating jobs, preserving jobs and training students for high skill positions in geospatial technology. Leaf-off lidar data were acquired at 1-m resolution of the Tennessee portion of the Great Smoky Mountain National Park (GRSM) and adjacent Foothills Parkway. This 1400-sq. km. area is of high priority for national/global interests due to biodiversity, rare and endangered species and protection of some of the last remaining virgin forest in the U.S. High spatial resolution (30 cm) leaf-off 4-band multispectral orthoimages also were acquired for both the Chattahoochee National Forest in north Georgia and the entire GRSM. The data are intended to augment the National Elevation Dataset and orthoimage database of The National Map with information that can be used by many researchers in applications of LiDAR point clouds, high resolution DEMs and or-thoimage mosaics. Graduate and undergraduate students were involved at every stage of the workflow in order to provide then with high level technical educational and professional experience in preparation for entering the geospatial workforce. This paper will present geospatial workflow strategies, multi-team coordination, distance-learning training and industry-academia partnership.
PHOTOGRAMMETRY – REMOTE SENSING AND GEOINFORMATION
Authors: Lazaridou M. A. , Patmio E. N.
Published: August 2012

Abstract. Earth and its environment are studied by different scientific disciplines as geosciences, science of engineering, social sciences, geography, etc. The study of the above, beyond pure scientific interest, is useful for the practical needs of man. Photogrammetry and Remote Sensing (defined by Statute II of ISPRS) is the art, science, and technology of obtaining reliable information from non-contact imaging and other sensor systems about the Earth and its environment, and other physical objects and of processes through recording, measuring, analyzing and representation. Therefore, according to this definition, photogrammetry and remote sensing can support studies of the above disciplines for acquisition of geoinformation. This paper concerns basic concepts of geosciences (geomorphology, geology, hydrology etc), and the fundamentals of photogrammetry-remote sensing, in order to aid the understanding of the relationship between photogrammetry-remote sensing and geoinformation and also structure curriculum in a brief, concise and coherent way. This curriculum can represent an appropriate research and educational outline and help to disseminate knowledge in various directions and levels. It resulted from our research and educational experience in graduate and post-graduate level (post-graduate studies relative to the protection of environment and protection of monuments and historical centers) in the Lab. of Photogrammetry – Remote Sensing in Civil Engineering Faculty of Aristotle University of Thessaloniki.
EDUSERV – THE EDUCATION SERVICE OF EUROSDR: SHARING EXPERIENCE FOR CAPACITY BUILDING
Authors: Fritsch D. , Mooney K. , Oestman A.
Published: August 2012

Abstract. This paper describes EduServ, the Education Service of EuroSDR – a European spatial data research organisation whose aim is to address the research needs of spatial data provision in Europe. With a current membership coming from seventeen European countries and a strong working relationship with related European organisations, EuroSDR has amassed considerable experience in addressing the extent and nature of this need. In order to facilitate the transfer of outcomes of EuroSDR research activities to the user domain, e.g. to key personnel in geographic information (GI) production organisations and industry, EuroSDR commenced this annual series of elearning courses in 2002. The Internet courses are preceded by a seminar at which participants meet tutors and receive guidelines for following the courses from their own locations. Delivery of the two-week courses requires an acceptable level of Internet connectivity, which exists in most member countries. EuroSDR is aware, however, that should such courses be shared internationally, other forms of communication will need to be addressed, such as satellite broadcasting. This would require effective collaboration with related organisations with experience with this means of communication. EduServ courses are offered in two successive years. During the courses, participants enjoy access to course tutors with a 24-hour response to queries guaranteed. Thereafter, course material for these courses is made available online. EuroSDR continues to work with past course tutors to ensure that this valuable resource is maintained as an effective and sustainable archive. This paper is aimed at sharing EuroSDR's experience in distance education with the wider scientific community with a view to its applicability to a global audience, whereby instead of sharing expertise within the GI community in Europe, European mapping agencies can share their knowledge and experience with the international GI community.
CLASSIFICATION OF ROOF MATERIALS USING HYPERSPECTRAL DATA
Authors: Chisense C.
Published: August 2012

Abstract. Mapping of surface materials in urban areas using aerial imagery is a challenging task. This is because there are numerous materials present in relatively small regions. Hyperspectral data features a fine spectral resolution and thus has a significant capability for automatic identification and mapping of urban surface materials. In this study an approach for identification of roof surface materials using hyperspectral data is presented. The study is based on an urban area in Ludwigsburg, Germany, using a HyMap data set recorded during the HyMap campaign in August, 2010. Automatisierte Liegenschaftskarte (ALK) vector data with a building layer is combined with the HyMap data to limit the analysis to roofs. A spectral library for roofs is compiled based on field and image measurements. In the roof material identification process, supervised classification methods, namely spectral angle mapper and spectral information divergence and the object oriented ECHO (extraction and classification of homogeneous objects) approach are compared. In addition to the overall shape of spectral curves, position and strength of absorptions features are used to enhance material identification. The discriminant analysis feature extraction method is applied to the HyMap data in order to identify features (band combinations) suitable for discriminating between the target classes. The identified optimal features are used to create a new data set which is later classified using the ECHO classifier. The classification results with respect to material types of roofs are presented in this study. The most important results are evaluated using orthophotos, probability maps and field visits.
Robust Metric based Anomaly Detection in Kernel Feature Space
Authors: Du B. , Zhang L. , Xin H.
Published: August 2012

Abstract. This thesis analyzes the anomalous measurement metric in high dimension feature space, where it is supposed the Gaussian assumption for state-of-art mahanlanobis algorithms is reasonable. The realization of the detector in high dimension feature space is by kernel trick. Besides, the masking and swamping effect is further inhibited by an iterative approach in the feature space. The proposed robust metric based anomaly detection presents promising performance in hyperspectral remote sensing images: the separability between anomalies and background is enlarged; background statistics is more concentrated, and immune to the contamination by anomalies.
ROAD CLASSIFICATION AND CONDITION DETERMINATION USING HYPERSPECTRAL IMAGERY
Authors: Mohammadi M.
Published: August 2012

Abstract. Hyperspectral data has remarkable capabilities for automatic identification and mapping of urban surface materials because of its high spectral resolution. It includes a wealth of information which facilitates an understanding of the ground material properties. For identification of road surface materials, information about their relation to hyperspectral sensor measurements is needed. In this study an approach for classification of road surface materials using hyperspectral data is developed. The condition of the road surface materials, in particular asphalt is also investigated. Hyperspectral data with 4m spatial resolution of the city of Ludwigsburg, Germany consisting of 125 bands (wavelength range of 0.4542μm to 2.4846 μm) is used. Different supervised classification methods such as spectral angle mapper are applied based on a spectral library established from field measurements and in-situ inspection. It is observed that using the spectral angle mapper approach with regions of interest is helpful for road surface material identification. Additionally, spectral features are tested using their spectral functions in order to achieve better classification results. Spectral functions such as mean and standard deviation are suitable for discriminating asphalt, concrete and gravel. Different asphalt conditions (good, intermediate and bad) are distinguished using the spectral functions such as mean and image ratio. The mean function gives reliable results. Automatisierte Liegenschaftskarte (ALK) vector data for roads is integrated in order to confine the analysis to roads. Reliable reference spectra are useful in evaluation of classification results for spectrally similar road surface materials. The classification results are assessed using orthophotos and field visits information.
LAND COVER INFORMATION EXTRACTION USING LIDAR DATA
Authors: Shaker A. , El-Ashmawy N.
Published: August 2012

Abstract. Light Detection and Ranging (LiDAR) systems are used intensively in terrain surface modelling based on the range data determined by the LiDAR sensors. LiDAR sensors record the distance between the sensor and the targets (range data) with a capability to record the strength of the backscatter energy reflected from the targets (intensity data). The LiDAR sensors use the near-infrared spectrum range which has high separability in the reflected energy from different targets. This characteristic is investigated to implement the LiDAR intensity data in land-cover classification. The goal of this paper is to investigate and evaluates the use of LiDAR data only (range and intensity data) to extract land cover information. Different bands generated from the LiDAR data (Normal Heights, Intensity Texture, Surfaces Slopes, and PCA) are combined with the original data to study the influence of including these layers on the classification accuracy. The Maximum likelihood classifier is used to conduct the classification process for the LiDAR Data as one of the best classification techniques from literature. A study area covering an urban district in Burnaby, British Colombia, Canada, is selected to test the different band combinations to extract four information classes: buildings, roads and parking areas, trees, and low vegetation (grass) areas. The results show that an overall accuracy of more than 70% can be achieved using the intensity data, and other auxiliary data generated from the range and intensity data. Bands of the Principle Component Analysis (PCA) are also created from the LiDAR original and auxiliary data. Similar overall accuracy of the results can be achieved using the four bands extracted from the Principal Component Analysis (PCA).
COMBINATION OF GENETIC ALGORITHM AND DEMPSTER-SHAFER THEORY OF EVIDENCE FOR LAND COVER CLASSIFICATION USING INTEGRATION OF SAR AND OPTICAL SATELLITE IMAGERY
Authors: Chu H. T. , Ge L.
Published: August 2012

Abstract. The integration of different kinds of remotely sensed data, in particular Synthetic Aperture Radar (SAR) and optical satellite imagery, is considered a promising approach for land cover classification because of the complimentary properties of each data source. However, the challenges are: how to fully exploit the capabilities of these multiple data sources, which combined datasets should be used and which data processing and classification techniques are most appropriate in order to achieve the best results. In this paper an approach, in which synergistic use of a feature selection (FS) methods with Genetic Algorithm (GA) and multiple classifiers combination based on Dempster-Shafer Theory of Evidence, is proposed and evaluated for classifying land cover features in New South Wales, Australia. Multi-date SAR data, including ALOS/PALSAR, ENVISAT/ASAR and optical (Landsat 5 TM+) images, were used for this study. Textural information were also derived and integrated with the original images. Various combined datasets were generated for classification. Three classifiers, namely Artificial Neural Network (ANN), Support Vector Machines (SVMs) and Self-Organizing Map (SOM) were employed. Firstly, feature selection using GA was applied for each classifier and dataset to determine the optimal input features and parameters. Then the results of three classifiers on particular datasets were combined using the Dempster-Shafer theory of Evidence. Results of this study demonstrate the advantages of the proposed method for land cover mapping using complex datasets. It is revealed that the use of GA in conjunction with the Dempster-Shafer Theory of Evidence can significantly improve the classification accuracy. Furthermore, integration of SAR and optical data often outperform single-type datasets.
NEW COMBINED PIXEL/OBJECT-BASED TECHNIQUE FOR EFFICIENT URBAN CLASSSIFICATION USING WORLDVIEW-2 DATA
Authors: Elsharkawy A. , Elhabiby M. , El-Sheimy N.
Published: August 2012

Abstract. The new advances of having eight bands satellite mission similar to WorldView-2, WV-2, give the chance to address and solve some of the traditional problems related to the low spatial and/or spectral resolution; such as the lack of details for certain features or the inability of the conventional classifiers to detect some land-cover types because of missing efficient spectrum information and analysis techniques. High-resolution imagery is particularly well suited to urban applications. High spectral and spatial resolution of WorldView-2 data introduces challenges in detailed mapping of urban features. Classification of Water, Shadows, Red roofs and concrete buildings spectrally exhibit significant confusion either from the high similarity in the spectral response (e.g. water and Shadows) or the similarity in material type (e.g. red roofs and concrete buildings). This research study assesses the enhancement of the classification accuracy and efficiency for a data set of WorldView-2 satellite imagery using the full 8-bands through integrating the output of classification process using three band ratios with another step involves an object-based technique for extracting shadows, water, vegetation, building, Bare soil and asphalt roads. Second generation curvelet transform will be used in the second step, specifically to detect buildings' boundaries, which will aid the new algorithm of band ratios classification through efficient separation of the buildings. The combined technique is tested, and the preliminary results show a great potential of the new bands in the WV-2 imagery in the separation between confusing classes such as water and shadows, and the testing is extended to the separation between bare soils and asphalt roads. The Integrated band ratio-curvelet transform edge detection techniques increased the percentage of building detection by more than 30%.
RANDOM FORESTS-BASED FEATURE SELECTION FOR LAND-USE CLASSIFICATION USING LIDAR DATA AND ORTHOIMAGERY
Authors: Guan H. , Yu J. , Li J. , Luo L.
Published: August 2012

Abstract. The development of lidar system, especially incorporated with high-resolution camera components, has shown great potential for urban classification. However, how to automatically select the best features for land-use classification is challenging. Random Forests, a newly developed machine learning algorithm, is receiving considerable attention in the field of image classification and pattern recognition. Especially, it can provide the measure of variable importance. Thus, in this study the performance of the Random Forests-based feature selection for urban areas was explored. First, we extract features from lidar data, including height-based, intensity-based GLCM measures; other spectral features can be obtained from imagery, such as Red, Blue and Green three bands, and GLCM-based measures. Finally, Random Forests is used to automatically select the optimal and uncorrelated features for landuse classification. 0.5-meter resolution lidar data and aerial imagery are used to assess the feature selection performance of Random Forests in the study area located in Mannheim, Germany. The results clearly demonstrate that the use of Random Forests-based feature selection can improve the classification performance by the selected features.
SPATIAL INTERPOLATION AS A TOOL FOR SPECTRAL UNMIXING OF REMOTELY SENSED IMAGES
Authors: Xi L. , Xiaoling C.
Published: August 2012

Abstract. Super resolution-based spectral unmixing (SRSU) is a recently developed method for spectral unmixing of remotely sensed imagery, but it is too complex to implement for common users who are interested in land cover mapping. This study makes use of spatial interpolation as an alternative approach to achieve super resolution reconstruction in SRSU. An ASTER image with three spectral bands was used as the test data. The algorithm is evaluated using root mean square error (RMSE) compared with linear spectral unmixing and hard classification. The result shows that the proposed algorithm has higher unmixing accuracy than those of the other comparative algorithms, and it is proved as an efficient and convenient spectral unmixing tool of remotely sensed imagery.
RESEARCH ON DIFFERENTIAL CODING METHOD FOR SATELLITE REMOTE SENSING DATA COMPRESSION
Authors: Lin Z. J. , Yao N. , Deng B. , Wang C. Z. , Wang J. H.
Published: August 2012

Abstract. Data compression, in the process of Satellite Earth data transmission, is of great concern to improve the efficiency of data transmission. Information amounts inherent to remote sensing images provide a foundation for data compression in terms of information theory. In particular, distinct degrees of uncertainty inherent to distinct land covers result in the different information amounts. This paper first proposes a lossless differential encoding method to improve compression rates. Then a district forecast differential encoding method is proposed to further improve the compression rates. Considering the stereo measurements in modern photogrammetry are basically accomplished by means of automatic stereo image matching, an edge protection operator is finally utilized to appropriately filter out high frequency noises which could help magnify the signals and further improve the compression rates. The three steps were applied to a Landsat TM multispectral image and a set of SPOT-5 panchromatic images of four typical land cover types (i.e., urban areas, farm lands, mountain areas and water bodies). Results revealed that the average code lengths obtained by the differential encoding method, compared with Huffman encoding, were more close to the information amounts inherent to remote sensing images. And the compression rates were improved to some extent. Furthermore, the compression rates of the four land cover images obtained by the district forecast differential encoding method were nearly doubled. As for the images with the edge features preserved, the compression rates are average four times as large as those of the original images.
INSAR ATMOSPHERIC DELAY MIGITIGATION BY GPS; CASE STUDY IZMIT EARTQUAKE INTERFEROGRAMS
Authors: Altın M. U. , Tari E. , Ge L.
Published: August 2012

Abstract. The Propagation delay when radar signals travel from the troposphere has been one of the major limitations for the applications of high accuracy Interferometric Synthetic Aparture Radar (InSAR). In this GPS data used for defining meteorological effects on radio signals. 1999 Izmit earthquake is chosen for the case study of tropospheric effects on InSAR images according to previous studies. Due to process of GPS data with BERNESE tropospheric delay model gained and compared with ROI_PAC processed interferograms and it can easy figured out that the delay amounts are really reliable. As a result of this study, importance of atmospheric change in Turkey climate will be pointed out using SAR and GPS data integration with meteorological aspects.
ALBEDO PATTERN RECOGNITION AND TIME-SERIES ANALYSES IN MALAYSIA
Authors: Salleh S. A. , Abd Latif Z. , Wan Mohd W. M. N. , Chan A.
Published: August 2012

Abstract. Pattern recognition and time-series analyses will enable one to evaluate and generate predictions of specific phenomena. The albedo pattern and time-series analyses are very much useful especially in relation to climate condition monitoring. This study is conducted to seek for Malaysia albedo pattern changes. The pattern recognition and changes will be useful for variety of environmental and climate monitoring researches such as carbon budgeting and aerosol mapping. The 10 years (2000–2009) MODIS satellite images were used for the analyses and interpretation. These images were being processed using ERDAS Imagine remote sensing software, ArcGIS 9.3, the 6S code for atmospherical calibration and several MODIS tools (MRT, HDF2GIS, Albedo tools). There are several methods for time-series analyses were explored, this paper demonstrates trends and seasonal time-series analyses using converted HDF format MODIS MCD43A3 albedo land product. The results revealed significance changes of albedo percentages over the past 10 years and the pattern with regards to Malaysia's nebulosity index (NI) and aerosol optical depth (AOD). There is noticeable trend can be identified with regards to its maximum and minimum value of the albedo. The rise and fall of the line graph show a similar trend with regards to its daily observation. The different can be identified in term of the value or percentage of rises and falls of albedo. Thus, it can be concludes that the temporal behavior of land surface albedo in Malaysia have a uniform behaviours and effects with regards to the local monsoons. However, although the average albedo shows linear trend with nebulosity index, the pattern changes of albedo with respects to the nebulosity index indicates that there are external factors that implicates the albedo values, as the sky conditions and its diffusion plotted does not have uniform trend over the years, especially when the trend of 5 years interval is examined, 2000 shows high negative linear trend relationship (R2 = 0.8017), while in 2005 the R2 is 0.4428 of positive linear trend relationship and in 2009 its negative relationship has remarkably change when the R2 is 0.9663 according to the second order polynomial trend line.
A SEMIAUTOMATIC ANOMALOUS CHANGE DETECTION METHOD FOR MONITORING AIMS
Authors: Artese G. , Achilli V. , Fabris M. , Perrelli M.
Published: August 2012

Abstract. In the framework of the development of a web site devoted to the documentation and monitoring of the cultural heritage (above all monumental buildings), a semiautomatic method for anomalous change detection has been set up. The method uses the grouping of the image difference values, to detect both small and diffused changes. Three tests are described to evaluate the performances of the method. The results show that good performances are obtained in case of cloudy days, while the presence of shadows requires the interpretation of an operator to distinguish true and false changes.
SEASONAL DIFFERENCES IN SPATIAL SCALES OF CHLOROPHYLL-A CONCENTRATION IN LAKE TAIHU，CHINA
Authors: Bao Y. , Tian Q. , Sun S. , Wei H. , Tian J.
Published: August 2012

Abstract. Spatial distribution of chlorophyll-a (chla) concentration in Lake Taihu is non-uniform and seasonal variability. Chla concentration retrieval algorithms were separately established using measured data and remote sensing images (HJ-1 CCD and MODIS data) in October 2010, March 2011, and September 2011. Then parameters of semi- variance were calculated on the scale of 30m, 250m and 500m for analyzing spatial heterogeneity in different seasons. Finally, based on the definitions of Lumped chla (chlaL) and Distributed chla (chlaD), seasonal model of chla concentration scale error was built. The results indicated that: spatial distribution of chla concentration in spring was more uniform. In summer and autumn, chla concentration in the north of the lake such as Meiliang Bay and Zhushan Bay was higher than that in the south of Lake Taihu. Chla concentration on different scales showed the similar structure in the same season, while it had different structure in different seasons. And inversion chla concentration from MODIS 500m had a greater scale error. The spatial scale error changed with seasons. It was higher in summer and autumn than that in spring. The maximum relative error can achieve 23%.
KERNEL-COMPOSITION FOR CHANGE DETECTION IN MEDIUM RESOLUTION REMOTE SENSING DATA
Authors: Braun A. C. , Weidner U. , Hinz S.
Published: August 2012

Abstract. A framework for multitemporal change detection based on kernel-composition is applied to a multispectral-multitemporal classification scenario, evaluated and compared to traditional change detection approaches. The framework makes use of the fact that images of different points in time can be used as input data sources for kernel-composition – a data fusion approach typically used with kernel based classifiers like support vector machines (SVM). The framework is used to analyze the growth of a limestone pit in the Upper Rhine Graben (West Germany). Results indicate that the highest accuracy rates are produced by the kernel based framework. The approach produces the least number of false positives and gives the most convincing overall impression.
METHODS FOR MULTITEMPORAL ANALYSIS OF SATELLITE DATA AIMED AT ENVIRONMENTAL RISK MONITORING
Authors: Caprioli M. , Scognamiglio A.
Published: August 2012

Abstract. In the last years the topic of Environmental monitoring has raised a particular importance, also according to minor short-term stability and predictability of climatic events. Facing this situation, often in terms of emergency, involves high and unpredictable costs for public Agencies. Prevention of damages caused by natural disasters does not regard only weather forecasts, but requires constant attention and practice of monitoring and control of human activity on territory. Practically, the problem is not knowing if and when an event will affect a determined area, but recognizing the possible damages if this event happened, by adopting the adequate measures to reduce them to a minimum, and requiring the necessary tools for a timely intervention. On the other hand, the surveying technologies should be the most possible accurate and updatable in order to guarantee high standards, involving the analysis of a great amount of data. The management of such data requires the integration and calculation systems with specialized software and fast and reliable connection and communication networks. To solve such requirements, current satellite technology, with recurrent data acquisition for the timely generation of cartographic products updated and coherent to the territorial investigation, offers the possibility to fill the temporal gap between the need of urgent information and official reference information. Among evolved image processing techniques, Change detection analysis is useful to facilitate individuation of environmental temporal variations, contributing to reduce the users intervention by means of the processes automation and improving in a progressive way the qualitative and quantitative accuracy of results. The research investigate automatic methods on land cover transformations by means of "Change detection" techniques executable on satellite data that are heterogeneous for spatial and spectral resolution with homogenization and registration in an unique digital information environment. In the present work we tested some areas of study particularly interesting for the knowledge of the morphology changes of land cover, in particular the area of Fasano in Apulia Region (Italy) and protected area of the Park of Alta Murgia, both of them with frequent episodes of land transformation. We tested the usability of heterogeneous and freely available images to realize a DEM extraction process to achieve fast and low cost system of analysis. We used archival stereo-pairs Ikonos and LIDAR survey comparing with Aerial photogrammetric DEM extraction.
SOURCES OF ARTEFACTS IN SYNTHETIC APERTURE RADAR INTERFEROMETRY DATA SETS
Authors: Becek K. , Borkowski A.
Published: August 2012

Abstract. In recent years, much attention has been devoted to digital elevation models (DEMs) produced using Synthetic Aperture Radar Interferometry (InSAR). This has been triggered by the relative novelty of the InSAR method and its world-famous product—the Shuttle Radar Topography Mission (SRTM) DEM. However, much less attention, if at all, has been paid to sources of artefacts in SRTM. In this work, we focus not on the missing pixels (null pixels) due to shadows or the layover effect, but rather on outliers that were undetected by the SRTM validation process. The aim of this study is to identify some of the causes of the elevation outliers in SRTM. Such knowledge may be helpful to mitigate similar problems in future InSAR DEMs, notably the ones currently being developed from data acquired by the TanDEM-X mission. We analysed many cross-sections derived from SRTM. These cross-sections were extracted over the elevation test areas, which are available from the Global Elevation Data Testing Facility (GEDTF) whose database contains about 8,500 runways with known vertical profiles. Whenever a significant discrepancy between the known runway profile and the SRTM cross-section was detected, a visual interpretation of the high-resolution satellite image was carried out to identify the objects causing the irregularities. A distance and a bearing from the outlier to the object were recorded. Moreover, we considered the SRTM look direction parameter. A comprehensive analysis of the acquired data allows us to establish that large metallic structures, such as hangars or car parking lots, are causing the outliers. Water areas or plain wet terrains may also cause an InSAR outlier. The look direction and the depression angle of the InSAR system in relation to the suspected objects influence the magnitude of the outliers. We hope that these findings will be helpful in designing the error detection routines of future InSAR or, in fact, any microwave aerial- or space-based survey. The presence of outliers in SRTM was first reported in Becek, K. (2008). Investigating error structure of shuttle radar topography mission elevation data product, Geophys. Res. Lett., 35, L15403.
SEMI-AUTOMATED CLOUD/SHADOW REMOVAL AND LAND COVER CHANGE DETECTION USING SATELLITE IMAGERY
Authors: Sah A. K. , Sah B. P. , Honji K. , Kubo N. , Senthil S.
Published: August 2012

Abstract. Multi-platform/sensor and multi-temporal satellite data facilitates analysis of successive change/monitoring over the longer period and there by forest biomass helping REDD mechanism. The historical archive satellite imagery, specifically Landsat, can play an important role for historical trend analysis of forest cover change at national level. Whereas the fresh high resolution satellite, such as ALOS, imagery can be used for detailed analysis of present forest cover status. ALOS satellite imagery is most suitable as it offers data with optical (AVNIR-2) as well as SAR (PALSAR) sensors. AVNIR-2 providing data in multispectral modes play due role in extracting forest information. In this study, a semi-automated approach has been devised for cloud/shadow and haze removal and land cover change detection. Cloud/shadow pixels are replaced by free pixels of same image with the help of PALSAR image. The tracking of pixel based land cover change for the 1995-2009 period in combination of Landsat and latest ALOS data from its AVNIR-2 for the tropical rain forest area has been carried out using Decision Tree Classifiers followed by un-supervised classification. As threshold for tree classifier, criteria of NDVI refined by reflectance value has been employed. The result shows all pixels have been successfully registered to the pre-defined 6 categories; in accordance with IPCC definition; of land cover types with an overall accuracy 80 percent.
ON THE USE OF DUAL-CO-POLARIZED TERRASAR-X DATA FOR WETLAND MONITORING
Authors: Schmitt A. , Leichtle T. , Huber M. , Roth A.
Published: August 2012

Abstract. Today's SAR sensors provide a variety of different image modes particularly with regard to multipolarised acquisitions. Until now, each polarisation mode requires a special decomposition which is a severe drawback when designing processing chains. Therefore, a new description for multipolarized SAR data based on the well-known Kennaugh matrix was developed that enables the uniform description and processing of SAR data independent of its polarisation by separating backscattering strength from polarimetric information. This mathematical approach subsequently is extended to the processing of multitemporal SAR data in order to stabilize the polarimetric information over longer periods of time and to enhance temporal changes in the polarimetric backscattering. Because of the high sensitivity of the Kennaugh elements a novel multilooking technique based on the Gaussian pyramid is used that locally adapts the look factor and thus selects the optimal balance between radiometric accuracy and geometric resolution. This methodology is applied to two dual-co-polarized TerraSAR-X acquisitions over the RAMSAR testsite "Upper Rhine" in order to generate value-added products that help to map land cover and land cover changes in consequence of water level changes. The first results are very promising although the interpretation of the observed polarimetric changes is not yet validated. The aim of this paper is to present a further application of the (Differential) Kennaugh matrix which will be the kernel of a polarimetry and change detection processor to be implemented in the coming years.
OBJECT-BASED CHANGE DETECTION USING HIGH-RESOLUTION REMOTELY SENSED DATA AND GIS
Authors: Sofina N. , Ehlers M.
Published: August 2012

Abstract. High resolution remotely sensed images provide current, detailed, and accurate information for large areas of the earth surface which can be used for change detection analyses. Conventional methods of image processing permit detection of changes by comparing remotely sensed multitemporal images. However, for performing a successful analysis it is desirable to take images from the same sensor which should be acquired at the same time of season, at the same time of a day, and – for electro-optical sensors – in cloudless conditions. Thus, a change detection analysis could be problematic especially for sudden catastrophic events. A promising alternative is the use of vector-based maps containing information about the original urban layout which can be related to a single image obtained after the catastrophe. The paper describes a methodology for an object-based search of destroyed buildings as a consequence of a natural or man-made catastrophe (e.g., earthquakes, flooding, civil war). The analysis is based on remotely sensed and vector GIS data. It includes three main steps: (i) generation of features describing the state of buildings; (ii) classification of building conditions; and (iii) data import into a GIS. One of the proposed features is a newly developed 'Detected Part of Contour' (DPC). Additionally, several features based on the analysis of textural information corresponding to the investigated vector objects are calculated. The method is applied to remotely sensed images of areas that have been subjected to an earthquake. The results show the high reliability of the DPC feature as an indicator for change.
RADARGRAMMETRIC DIGITAL SURFACE MODELS GENERATION FROM TERRASAR-X IMAGERY: CASE STUDIES, PROBLEMS AND POTENTIALITIES
Authors: Capaldo P. , Crespi M. , Fratarcangeli F. , Nascetti A. , Pieralice F.
Published: August 2012

Abstract. The interest for the radargrammetric approach to Digital Surface Models (DSMs) generation has been growing in last years thanks to the availability of very high resolution imagery acquired by new SAR (Synthetic Aperture Radar) sensors, as COSMO-SkyMed, Radarsat-2 and TerraSAR-X, which are able to supply imagery up to 1 m ground resolution. DSMs radargrammetric generation approach consists of two basic steps, as for the standard photogrammetry applied to optical imagery: the imagery (at least a stereo pair) orientation and the image matching for the generation of the points cloud. The steps of the radargrammetric DSMs generation have been implemented into SISAR (Software per Immagini Satellitari ad Alta Risoluzione), a scientific software developed at Geodesy and Geomatics Institute of the University of Rome “La Sapienza”. Moreover, starting from the radargrammetric orientation model, a tool for the Rational Polynomial Coefficients (RPCs) for SAR images have been implemented. The possibility to generate RPCs, re-parametrizing a rigorous orientation model through a standardized set of coefficients which can be managed by a Rational Polynomial Coefficients (RPFs) model (similarly to optical high resolution imagery) sounds of particular interest since, at present, the most part of SAR imagery (except from Radarsat-2) is not supplied with RPCs, although the corresponding RPFs model is available in several commercial software. In particular the RPCs model has been used in the matching process and in the stereo restitution for the DSMs generation, with the advantage of shorter computational time. This paper discusses the application and the results of the implemented algorithm for radargrammetric DSMs generation from TerraSAR-X SpotLight imagery, acquired in Spotlight mode over Trento (Northern Italy). Urban and extra-urban (forested, cultivated) areas were considered in two different tiles, and a final overall accuracy ranging from 4.5 to 6 meters was achieved as regards the point clouds, enough well distributed independently from the land cover; moreover, it was highlighted the benefit to filter the originally derived points cloud with a global DSM as SRTM DEM, what leads to an accuracy improvement of about 20% paying a loss of matched points of about 10 %.
QUANTITATIVE ANALYSIS OF URBAN EXPANSION IN CENTRAL CHINA
Authors: Zeng Y. , Xu Y. , Li S. , He L. , Yu F. , Zhen Z. , Cai C.
Published: August 2012

Abstract. Quantifying urban expansion forms is important to understanding regional urbanization processes and urban planning. For this purpose, conventional landscape indices are commonly used for quantitative analysis of urban landscape patterns. However, these landscape indices only reflect information for one particular temporal phase of landscape patterns. This paper studies and quantifies the dynamic changes of urban landscape from 1993 to 2006 in Changsha-Zhuzhou-Xiangtan metropolitan areas in Hunan province of China using landscape expansion index (LEI), which contains information of the formation processes of landscape patterns. The results indicate that there are three types of urban expansions: infilling, edge-expansion and outlying in the study area. The change of proportion of the three urban expansion types reveals that urban expansion patterns have changed from a messy, dispersed early development phase to more compact and reasonable layout from 1993 to 2006. Moreover, the urban expansion modes varied in different periods. From 1993 to 1996, the edge-expansion and outlying were the main types of urban expansion forms, indicating an early stage of rapid urban developments. Comparing with the edge-expansion, the outlying expansion increased rapidly in this period, which indicates urban development is messy and dispersion. Overall, the edge-expansion was the major type of urban expansion form during the study period with outlying as the second and rapidly-increasing major form of expansion prior to 1998, which indicates urbanization is in the early stage of rapid urban developments, and infilling as the second and rapidly-increasing major form of expansion after 1998.