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Pillala S.K.,Advanced Data Processing Research Institute | Ravikanti C.,Advanced Data Processing Research Institute | Mishra N.,Advanced Data Processing Research Institute | Janjam S.,Advanced Data Processing Research Institute | Varadan G.,Advanced Data Processing Research Institute
International Journal of Remote Sensing | Year: 2012

A generalized search scheme for automatic registration of level-1 data of multires-olution and multi-sensor remote-sensing data was proposed. The robustness and time efficiency of the automatic registration process is critically dependant on the search scheme for identification of control points. The proposed method consists of three levels of search ranging from coarser to finer. This process was found to be capable of registering images to sub-pixel level which are independent of rotation and scale variations, and also translation that vary by few metres to kilometres. In order to reduce the low pass effect due to multiple transformations involved in the multi-level registration process, a direct correspondence between the reference image and target image was established so that a single resampling needs to be applied. This correspondence also helps to generate products at any desired pixel size and to keep the original resolution intact. In this scheme mutual information (MI) is used as a similarity measure and a non-rigid transformation, thin plate spline (TPS), is used for achieving sub-pixel registration accuracies. MI is found to be better for identification of match points even for images that are radiometrically nonlinear. Unlike global transformation methods, use of non-rigid transformations such as TPS achieves sub-pixel accuracy in the moderate hilly regions as well as high hilly regions where relief displacements are high, provided sufficient number of control points are generated. However TPS transformation demands accurate control points. A robust method for detection of inaccurate control points was adopted and explained in the paper. The scheme was tested on a number of combinations of remote sensing data of the same resolution and different resolution datasets, namely Cartosat-1 with Liss-4 of Resourcesat-1, Landsat Thematic Mapper (TM) with IRS-1C/1D and Cartosat-1 with Enhanced Thematic Mapper (ETM). The results are presented along with accuracies achieved.


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.


Radhadevi P.V.,Advanced Data Processing Research Institute | Solanki S.S.,Advanced Data Processing Research Institute | Nagasubramanian V.,Advanced Data Processing Research Institute | Mahapatra A.,Advanced Data Processing Research Institute | And 5 more authors.
Photogrammetric Engineering and Remote Sensing | Year: 2010

Important considerations for large scale mapping from satellite images are information content and geometric fidelity. Cartosat series of satellites with stereo mapping capability have become the mainstay towards large scale mapping for urban and rural applications. Algorithms for processing of high-resolution Indian remote sensing satellite data has been developed at ADRIN and is used for operational generation of data products. Variations in the sensor model with respect to the viewing geometries of Cartosat-1 and Cartosat-2 are explained in the paper. Finally, an assessment of the mapping potential of the satellites is discussed. The geometric accuracy achieved from Cartosat-1 and Cartosat-2 images over the same checkpoints are compared. DEM, geometric accuracy, and capability for topographic feature capture are good enough for making 1:10 000 and 1:7 000 scale maps from Cartosat-1 and Cartosat-2, respectively. Based on the error estimation and analysis, it is concluded that if the strict photogrammetric processing model and ground control points are employed, high-resolution satellite imagery can be used for the generation and update of topographic maps of scale 1:10 000 and larger. © 2010 American Society for Photogrammetry and Remote Sensing.


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.


Radhadevi P.V.,Advanced Data Processing Research Institute | Solanki S.S.,Advanced Data Processing Research Institute | Akilan A.,Advanced Data Processing Research Institute | Jyothi M.V.,Advanced Data Processing Research Institute | Nagasubramanian V.,Advanced Data Processing Research Institute
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2016

Resourcesat-2 (RS-2) has successfully completed five years of operations in its orbit. This satellite has multi-resolution and multi-spectral capabilities in a single platform. A continuous and autonomous co-registration, geo-location and radiometric calibration of image data from different sensors with widely varying view angles and resolution was one of the challenges of RS-2 data processing. On-orbit geometric performance of RS-2 sensors has been widely assessed and calibrated during the initial phase operations. Since then, as an ongoing activity, various geometric performance data are being generated periodically. This is performed with sites of dense ground control points (GCPs). These parameters are correlated to the direct geo-location accuracy of the RS-2 sensors and are monitored and validated to maintain the performance. This paper brings out the geometric accuracy assessment, calibration and validation done for about 500 datasets of RS-2. The objectives of this study are to ensure the best absolute and relative location accuracy of different cameras, location performance with payload steering and co-registration of multiple bands. This is done using a viewing geometry model, given ephemeris and attitude data, precise camera geometry and datum transformation. In the model, the forward and reverse transformations between the coordinate systems associated with the focal plane, payload, body, orbit and ground are rigorously and explicitly defined. System level tests using comparisons to ground check points have validated the operational geo-location accuracy performance and the stability of the calibration parameters.


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.


Radhadevi P.V.,Advanced Data Processing Research Institute | Solanki S.S.,Advanced Data Processing Research Institute | Jyothi M.V.,Advanced Data Processing Research Institute | Varadan G.,Advanced Data Processing Research Institute
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2014

Continuous and automated co-registration and geo-tagging of images from multiple bands of Liss-4 camera is one of the interesting challenges of Resourcesat-2 data processing. Three arrays of the Liss-4 camera are physically separated in the focal plane in alongtrack direction. Thus, same line on the ground will be imaged by extreme bands with a time interval of as much as 2.1 seconds. During this time, the satellite would have covered a distance of about 14 km on the ground and the earth would have rotated through an angle of 30". A yaw steering is done to compensate the earth rotation effects, thus ensuring a first level registration between the bands. But this will not do a perfect co-registration because of the attitude fluctuations, satellite movement, terrain topography, PSM steering and small variations in the angular placement of the CCD lines (from the pre-launch values) in the focal plane. This paper describes an algorithm based on the viewing geometry of the satellite to do an automatic band to band registration of Liss-4 MX image of Resourcesat-2 in Level 1A. The algorithm is using the principles of photogrammetric collinearity equations. The model employs an orbit trajectory and attitude fitting with polynomials. Then, a direct geo-referencing with a global DEM with which every pixel in the middle band is mapped to a particular position on the surface of the earth with the given attitude. Attitude is estimated by interpolating measurement data obtained from star sensors and gyros, which are sampled at low frequency. When the sampling rate of attitude information is low compared to the frequency of jitter or micro-vibration, images processed by geometric correction suffer from distortion. Therefore, a set of conjugate points are identified between the bands to perform a relative attitude error estimation and correction which will ensure the internal accuracy and co-registration of bands. Accurate calculation of the exterior orientation parameters with GCPs is not required. Instead, the relative line of sight vector of each detector in different bands in relation to the payload is addressed. With this method a band to band registration accuracy of better than 0.3 pixels could be achieved even in high hill areas.


Akilan A.,Advanced Data Processing Research Institute | Reddy D.S.,Advanced Data Processing Research Institute | Nagasubramanian V.,Advanced Data Processing Research Institute | Radhadevi P.V.,Advanced Data Processing Research Institute | Varadan G.,Advanced Data Processing Research Institute
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2014

Cartosat-1 provides stereo images of spatial resolution 2.5m with high fidelity of geometry. Stereo camera on the spacecraft has look angles of +26 degree and -5 degree respectively that yields effective along track stereo. Any DSM generation algorithm can use the stereo images for accurate 3D reconstruction and measurement of ground. Dense match points and pixel-wise matching are prerequisite in DSM generation to capture discontinuities and occlusions for accurate 3D modelling application. Epipolar image matching reduces the computational effort from two dimensional area searches to one dimensional. Thus, epipolar rectification is preferred as a pre-processing step for accurate DSM generation. In this paper we explore a method based on SIFT and RANSAC for epipolar rectification of cartosat-1 stereo images.


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.


Radhadevi P.V.,Advanced Data Processing Research Institute | Solanki S.S.,Advanced Data Processing Research Institute | Nagasubramanian V.,Advanced Data Processing Research Institute | Sudheer Reddy D.,Advanced Data Processing Research Institute | And 3 more authors.
Planetary and Space Science | Year: 2013

This paper describes the development of an algorithm for geometric correction of Terrain Mapping Camera (TMC) imagery of Chandrayaan-1 (CH-1). The correction is based on a rigorous sensor model. The algorithm incorporates the camera geometry model, satellite data and lunar control points in a rigorous bundle adjustment updating the satellite model parameters. The model is tested for different strips over the polar and equatorial regions and the results are presented. The planimetric control is identified from the 100 m/pixel USGS Clementine base map mosaic and vertical control is derived from Lunar Orbiter Laser Altimeter (LOLA) data. RMS error of the order 200-300 m in latitude, longitude and height with respect to the references could be achieved using the sensor model with few distributed controls over the strip. The model is used for the orientation of long strips with little or no degradation in orientation quality attainable with a short scene. The results are representative of the stability of the platform and potential of CH-1 for accurate lunar mapping. The algorithm for geometric correction described in this paper is a part of Lunar Mapping System (LMS), which will handle full-pass data for operational generation of Digital Elevation Models (DEM) and Ortho products from TMC images of CH-1. © 2013 Elsevier B.V. All rights reserved.

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