Sub Pixel Classification Analysis for Hyperspectral Data (Hyperion) for Cairo Region, Egypt

Traditional hard classifiers in remote sensing applications can label image pixels only with one class, so landcover (e.g. trees) can only be recorded as either present or absent. This approach might lead to inaccurate imageclassification and accordingly inaccurate land cover. The proposed analysis technique provides the relativeabundance of surface materials and the context within a pixel that may be a potential solution to effectivelyidentifying the land-cover distribution. This research is applied on the central region of Cairo using hyperspectralimage data, which provides a large amount of spectral information. A spectral mixture analysis approach is usedon Hyperion data (hyperspectral data) to produce abundance images representing the percentage of the existenceof each material/land cover with a pixel. The uniqueness of this study comes from the fact that it is the first timeHyperion data has been used to extract land cover in Egypt.Keywords: Spectral Mixture Analysis, Hyperspectral Data, Hyperion Data, Cairo, Egyp

A Developed Algorithm for Automating the Multiple Bands Multiple Endmember Selection of Hyperion data Applied on Central of Cairo, Egypt

This study attempts to provide an answer regarding the utility of Hyperion imagery in mapping urban settings in developed countries. The authors present a novel method for extracting quantitative land cover information at the sub-pixel level from hyperspectral or Hyperion imagery. The proposed method is based on the multiple endmember spectral mixture (MESMA) proposed by Roberts et al. (1998b), but extends it to handle the high-dimensional pixels characterizing hyperspectral images. The proposed method utilizes a multiband multiple endmember spectral mixture analysis (Multiband MESMA) model that allows for both spectral bands and endmembers to vary on a per-pixel basis across a hyperspectral image. The goal is to select an optimal subset of spectral bands that maximizes spectral separability among a candidate set of endmembers for a given pixel, and accordingly to minimize spectral confusion among modeled endmembers and increase the accuracy and physical representativeness of derived fractions for that pixel. The authors develop a tool to automate this method and test its utility in a case study using a Hyperion image of Central Cairo, Egypt. The EO-1 Hyperion hyperspectral sensor is the only source of hyperspectral data currently available for Cairo, unlike cities in Europe and North America, where multiple sources of such data generally exist. The study scene represents a very heterogeneous landscape and has an ecological footprint of a complex range of interrelated socioeconomic, environmental and urban dynamics. The results of this study show that Hyperion data, with its rich spectral information, can help address some of the limitations in automated mapping that are reported by previous studies. For this, proper bands and endmembers are selected and used within a multiple endmember, with a multiple-band SMA process to determine the best Root Mean Square Error (RMSE) and abundance percentages. This results in a better mapping of land cover extricated from hyperspectral imagery (Hyperion). Keywords: Spectral Mixture Analysis, Hyperspectral Data, Hyperion Data, Cairo, Egyp

Modelling of Spatial Big Data Analysis and Visualization

Today’s advanced survey tools open new approaches and opportunities for Geoscience researchers to create new Models, Systems and frameworks to support the lifecycle of special big data. Mobile Mapping Systems use LIDAR technology to provide efficient and accurate way to collect geographic features and its attribute from field, whichhelps city planning departments and surveyors to design and update city GIS maps with a high accuracy. It is not only about heterogenic increase in the volume of point cloud data, but also it refers to several other characteristics such as its velocity and variety. However,the vast amount of Point Cloud data gathered by Mobile Mapping Systemleads to new challenges for researches, innovation and business development to solve its five characters: Volume, Velocity, Variety, and Veracity then achievethe Value of SBD. Cloud Computing has provided a new paradigm to publish and consume new spatial models as a service plus big data utilities , services which can be utilized to overcome Point Cloud data analysis and visualization challenges. This paper presentsa model With Cloud-Based Spatial,big data Services,using spatial joinservices capabilities to relate the analysis results to its location on map,describe how Cloud Computing supports the visualizing and analyzing spatial big data and review the related scientific model’s examples

Applying Association Rules and Co-location Techniques on Geospatial Web Services

Most contemporary GIS have only very basic spatial analysis and data mining functionality and many are confined to analysis that involves comparing maps and descriptive statistical displays like histograms or pie charts. Emerging Web standards promise a network of heterogeneous yet interoperable Web Services. Web Services would greatly simplify the development of many kinds of data integration and knowledge management applications. Geospatial data mining describes the combination of two key market intelligence software tools: Geographical Information Systems and Data Mining Systems. This research aims to develop a Spatial Data Mining web service it uses rule association techniques and correlation methods to explore results of huge amounts of data generated from crises management integrated applications developed. It integrates between traffic systems, medical services systems, civil defense and state of the art Geographic Information Systems and Data Mining Systems functionality in an open, highly extensible, internet-enabled plug-in architecture. The Interoperability of geospatial data previously focus just on data formats and standards. The recent popularity and adoption of the Internet and Web Services has provided a new means of interoperability for geospatial information not just for exchanging data but for analyzing these data during exchange. An integrated, user friendly Spatial Data Mining System available on the internet via a web service offers exciting new possibilities for spatial decision making and geographical research to a wide range of potential users.   Keywords: Spatial Data Mining, Rule Association, Co-location, Web Services, Geospatial Dat

Stochastic Optimization Approaches for Solving Sudoku

In this paper the Sudoku problem is solved using stochastic search techniques and these are: Cultural Genetic Algorithm (CGA), Repulsive Particle Swarm Optimization (RPSO), Quantum Simulated Annealing (QSA) and the Hybrid method that combines Genetic Algorithm with Simulated Annealing (HGASA). The results obtained show that the CGA, QSA and HGASA are able to solve the Sudoku puzzle with CGA finding a solution in 28 seconds, while QSA finding a solution in 65 seconds and HGASA in 1.447 seconds. This is mainly because HGASA combines the parallel searching of GA with the flexibility of SA. The RPSO was found to be unable to solve the puzzle.Comment: 13 page

Ruminant Brucellosis in the Kafr El Sheikh Governorate of the Nile Delta, Egypt: Prevalence of a Neglected Zoonosis

Brucellosis is a zoonosis of mammals caused by bacteria of the genus Brucella. It is responsible for a vast global burden imposed on human health through disability and on animal productivity. In humans brucellosis causes a range of flu-like symptoms and chronic debilitating illness. In livestock brucellosis causes economic losses as a result of abortion, infertility and decreased milk production. The main routes for human infection are consumption of contaminated dairy products and contact with infected ruminants. The control of brucellosis in humans depends on its control in ruminants, for which accurate estimates of the frequency of infection are very useful, especially in areas with no previous frequency estimates. We studied the seroprevalence of brucellosis and its geographic distribution among domestic ruminants in one governorate of the Nile Delta region, Egypt. In the study area, the seroprevalence of ruminant brucellosis is very high and has probably increased considerably since the early 1990s. The disease is widespread but more concentrated around major animal markets. These findings question the efficacy of the control strategy in place and highlight the high infection risk for the animal and human populations of the area and the urgent need for an improved control strategy