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Kune R.,Advanced Data Processing Research Institute | Konugurthi P.K.,Advanced Data Processing Research Institute | Agarwal A.,University of Hyderabad | Chillarige R.R.,University of Hyderabad | Buyya R.,University of Melbourne
Software - Practice and Experience | Year: 2016

Advances in information technology and its widespread growth in several areas of business, engineering, medical, and scientific studies are resulting in information/data explosion. Knowledge discovery and decision-making from such rapidly growing voluminous data are a challenging task in terms of data organization and processing, which is an emerging trend known as big data computing, a new paradigm that combines large-scale compute, new data-intensive techniques, and mathematical models to build data analytics. Big data computing demands a huge storage and computing for data curation and processing that could be delivered from on-premise or clouds infrastructures. This paper discusses the evolution of big data computing, differences between traditional data warehousing and big data, taxonomy of big data computing and underpinning technologies, integrated platform of big data and clouds known as big data clouds, layered architecture and components of big data cloud, and finally open-technical challenges and future directions. © 2015 John Wiley & Sons, Ltd.


Kune R.,Advanced Data Processing Research Institute | Konugurthi P.,Advanced Data Processing Research Institute | Agarwal A.,University of Hyderabad | Chillarige R.R.,University of Hyderabad | Buyya R.,University of Melbourne
Proceedings - 2015 IEEE International Conference on Cloud Computing in Emerging Markets, CCEM 2015 | Year: 2015

Hadoop Distributed File System (HDFS) and MapReduce model have become de facto standard for large scale data organization and analysis. Existing model of data organization and processing in Hadoop using HDFS and MapReduce are ideally tailored for search and data parallel applications, for which there is no data dependency with neighboring/adjacent data. Many scientific applications such as image mining, data mining, knowledge data mining, satellite image processing etc., are dependent on adjacent data for processing and analysis. In this paper, we discuss the requirements of the overlapped data organization and propose XHAMI as a two phase extensions to HDFS and MapReduce programming model to address such requirements. We present the APIs and discuss their implementation specific to Image Processing (IP) domain in detail, followed by sample case studies of image processing functions along with the results. XHAMI though has little overheads in data storage and input/output operations, but greatly improves the system performance and simplifies the application development process. The proposed system works without any changes for the existing MapReduce models with zero overheads, and can be used for many domain specific applications where there is a requirement of overlapped data. © 2015 IEEE.


Chandrakanth R.,Advanced Data Processing Research Institute | Saibaba J.,Advanced Data Processing Research Institute | Varadan G.,Advanced Data Processing Research Institute | Raj P.A.,Osmania University
2011 International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping, M2RSM 2011 | Year: 2011

This paper proposes a methodology for fusion of high resolution satellite SAR and Optical Panchromatic images. The main objective of fusion is to bring together complementary information contained in SAR and Optical images. The paper discusses and illustrates the issues involved in merging and choosing of suitable approaches to overcome them. The choosing of proper fusion method was explained from the point of nature of SAR and Optical wave interaction with the surface and objective of fusion. Two methods are proposed in this paper one is based on Fourier filtering and the other is based on multi-resolution pyramid. The methodologies are applied on Cartosat-1 Panchromatic and TerraSAR-X images. The results and evaluation of the fusion based on entropy are presented. ©2011 IEEE.


Chandrakanth R.,Advanced Data Processing Research Institute | Saibaba J.,Advanced Data Processing Research Institute | Varadan G.,Advanced Data Processing Research Institute | Ananth Raj P.,Osmania University
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2011

Unlike PAN sharpening, the fusion of SAR with multispectral data involve use of non-overlapping spectral bands which poses certain inconsistencies viz. 1) radiometric differences due to their acquisition in entirely different spectral bands 2) geometric differences due to range and angular imaging of SAR and Optical sensors respectively. Apart from these, speckle noise and registration related factors will pose difficulties in the fusion. The geometric differences can be overcome to a certain extent by proper selection of acquisition parameters of the sensor. In this context feasibility conditions for proper fusion by selection of data is explained. In regard to radiometry this paper proposes a methodology that is based on multiresolution pyramids and spectral weighting functions. It has shown better performance in preservation of both spatial, spectral contents as well as better overall information content in the fused image. It has shown good balance in contrast between high frequency features of SAR and multispectral images. The results of the proposed method are compared with other methods like wavelet, Ehlers, PCA and IHS. The result of applying on TerraSAR-X SAR images with IRS-P6, Liss-4 multispectral images are analysed and illustrated in the paper. © 2011 IEEE.


Chandrakanth R.,Advanced Data Processing Research Institute | Saibaba J.,Advanced Data Processing Research Institute | Varadan G.,Advanced Data Processing Research Institute | Ananth Raj P.,Osmania University
IETE Journal of Research | Year: 2014

This paper presents a novel image fusion system for multisensor and multiband remote sensing data with an objective to synthesize an image which is either not feasible or not economical to obtain from single remote sensing satellite image. The fusion system focuses on objective, selection of data, pre-processing, registration, and fusion methodology with results. The paper also emphasizes on understanding of physics of remote sensing for meeting the objective as well as for success of fusion. In view of availability and archival of huge amount of remote sensing imagery from several satellites, different combinations for fusion from multisensor and multiband images are proposed. Due to use of images with differing modalities, resolution, and time of acquisition for fusion, a generalized and robust image registration methodology is adopted. Appropriate fusion methodologies are proposed for different fusion combinations and their results are illustrated and compared. Copyright © 2014 by the IETE.

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