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Co-Authors
- S. Manthira Moorthi
- R. Sivakumar
- Bimal K. Bhattacharya
- Robert O. Green
- Sadasiva Rao
- M. Saxena
- Shweta Sharma
- K. Ajay Kumar
- P. Srinivasulu
- Shashikant Sharma
- S. Bandyopadhyay
- Shantanu Bhatwadekar
- Raj Kumar
- Manoj K. Mishra
- Anurag Gupta
- Jinya John
- Bipasha P. Shukla
- Philip Dennison
- S. S. Srivastava
- Nitesh K. Kaushik
- Arundhati Misra
Journals
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Dhar, D.
- Co-Registration of LISS-4 Multispectral Band Data Using Mutual Information-Based Stochastic Gradient Descent Optimization
Abstract Views :236 |
PDF Views:89
Authors
Affiliations
1 Signal and Image Processing Area, Space Applications Centre, ISRO, Ahmedabad 380 015, IN
2 Department of Civil Engineering, SRM University, Kattankulathur 603 203, IN
1 Signal and Image Processing Area, Space Applications Centre, ISRO, Ahmedabad 380 015, IN
2 Department of Civil Engineering, SRM University, Kattankulathur 603 203, IN
Source
Current Science, Vol 113, No 05 (2017), Pagination: 877-888Abstract
We propose a solution for automatic co-registration of LISS-4 MX radiometrically conditioned multi-spectral images issue by considering an optimization problem in which mutual information-based approach is used. Co-registration of multi-spectral images from the same sensor may also be a tough problem to tackle, whenthe payload imaging geometry is complex. The multi-spectral images acquired by ISRO Resources at-1/2 LISS-4 MX class of sensors pose such problems and demand an automatic registration solution for system corrected product generation to cater to user needs. Optical remote sensing image registration is assisted by image geo-referencing or navigation information along with components such as feature detection, matching, correspondence, and resampling the input image to the reference geometry. Intensity-based methods employ an iterative registration framework,where similarity metric based image matching and correspondence is refined to find out optimum transform parameters. We could successfully employ mutual information-based adaptive stochastic gradient descent optimization algorithm to do sub-pixel level satellite image registration tasks by a careful choice of parameters and models related to metric, transform, optimizer, and interpolator in a robust image registration framework which is automatic for different terrain data. The performance is also compared to a recent scale invariant feature transform (SIFT)-based registration method.Keywords
Image Registration, LISS-4, Mutual Information, Optimization.References
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- Radhadevi, P. V., Solanki, S. S., Jyothi, M. V., Nagasubramanian, V. and Geeta, V., Automated co-registration of images from multiple bands of Liss-4 camera. ISPRS J. Photogramm. Remote Sensing, 2009, 64, 17–26.
- Pillala, S. K., Ravikanti, C., Mishra, N., Janja, S. and Geeta, V., A generalized search scheme for automatic registration of remote-sensing data. Int. J. Remote Sensing, 2012, 33, 490–501.
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- Pluim, J. P. W., Maintz, J. B. A. and Viergever, M. A., Mutual-Information-based registration of medical images: a survey. IEEE Trans. Med. Imaging, 2003, 22, 986–1004.
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- Klein, S., Pluim, J. P. W., Staring, M. and Viergever, M. A., Adaptive stochastic gradient descent optimisation for image registration. Int. J. Comput. Vis., 2009, 81, 227–239.
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- An Overview of AVIRIS-NG Airborne Hyperspectral Science Campaign Over India
Abstract Views :241 |
PDF Views:79
Authors
Bimal K. Bhattacharya
1,
Robert O. Green
2,
Sadasiva Rao
3,
M. Saxena
1,
Shweta Sharma
1,
K. Ajay Kumar
1,
P. Srinivasulu
3,
Shashikant Sharma
1,
D. Dhar
1,
S. Bandyopadhyay
4,
Shantanu Bhatwadekar
4,
Raj Kumar
1
Affiliations
1 Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, IN
2 Jet Propulsion Laboratory, California Institute of Technology, CA 91109, IN
3 National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 625, IN
4 Earth Observation Science Directorate, Indian Space Research Organisation, Bengaluru 560 231, IN
1 Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, IN
2 Jet Propulsion Laboratory, California Institute of Technology, CA 91109, IN
3 National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 625, IN
4 Earth Observation Science Directorate, Indian Space Research Organisation, Bengaluru 560 231, IN
Source
Current Science, Vol 116, No 7 (2019), Pagination: 1082-1088Abstract
The first phase of an airborne science campaign has been carried out with the Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) imaging spectrometer over 22,840 sq. km across 57 sites in India during 84 days from 16 December 2015 to 6 March 2016. This campaign was organized under the Indian Space Research Organisation (ISRO) and National Aeronautics and Space Administration (NASA) joint initiative for HYperSpectral Imaging (HYSI) programme. To support the campaign, synchronous field campaigns and ground measurements were also carried out over these sites spanning themes related to crop, soil, forest, geology, coastal, ocean, river water, snow, urban, etc. AVIRIS-NG measures the spectral range from 380 to 2510 nm at 5 nm sampling with a ground sampling distance ranging from 4 to 8 m and flight altitude of 4–8 km. On-board and ground-based calibration and processing were carried out to generate level 0 (L0) and level 1 (L1) products respectively. An atmospheric correction scheme has been developed to convert the measured radiances to surface reflectance (level 2). These spectroscopic signatures are intended to discriminate surface types and retrieve physical and compositional parameters for the study of terrestrial, aquatic and atmospheric properties. The results from this campaign will support a range of objectives, including demonstration of advanced applications for societal benefits, validation of models/techniques, development of state-of-the-art spectral libraries, testing and refinement of automated tools for users, and definition of requirements for future space-based missions that can provide this class of measurements routinely for a range of important applications.Keywords
Airborne Science Campaign, Hyperspectral Sensing, Imaging Spectrometer, Surface Reflectance.References
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- Retrieval of Atmospheric Parameters and Data-Processing Algorithms forAVIRIS-NG Indian Campaign Data
Abstract Views :212 |
PDF Views:83
Authors
Manoj K. Mishra
1,
Anurag Gupta
1,
Jinya John
1,
Bipasha P. Shukla
1,
Philip Dennison
2,
S. S. Srivastava
1,
Nitesh K. Kaushik
1,
Arundhati Misra
1,
D. Dhar
1
Affiliations
1 Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, IN
2 Department of Geography, University of Utah, Salt Lake City, UT, US
1 Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, IN
2 Department of Geography, University of Utah, Salt Lake City, UT, US
Source
Current Science, Vol 116, No 7 (2019), Pagination: 1089-1100Abstract
Applications of high-spatial resolution imaging spectrometer data acquired from the Airborne Visible/ Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) under India campaign 2015–16, require a thorough compensation for atmospheric absorption and scattering. The data-processing algorithms used for retrieving critically important atmospheric parameters, namely ‘water vapour and aerosol optical depth (AOD)’ over land and water surfaces are presented. Over land surfaces, the dark dense vegetation method and radiative transfer modelling are used for deriving spectral AOD for boxes of 20 × 20 pixels. For AOD retrieval over water surfaces, dark-target approximation is used with near-infrared and shortwave infrared measurements. Estimation of precipitable water vapour is carried out using short-wave hyperspectral measurements for each pixel. A differential absorption technique (continuum interpolated band ratio) has been used for this purpose. The retrieved AOD and water vapour values were compared with in situ sun-photometer and radiosonde data respectively, indicating good matches. Further, these parameters were used to derive ‘atmospherically corrected surface reflectance and remote sensing reflectance’, for land and water surface respectively, assuming horizontal surfaces having Lambertian reflectance.Keywords
Aerosol, Atmospheric Correction, Hyperspectral Imaging, Surface Reflectance, Water Vapour.References
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