Author Details

Scroll

Refine your search

Collections

Basic & Applied Science Collection

Journals

Current Science

Year

2017
2019

Authors

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All

Dhar, D.

Co-Registration of LISS-4 Multispectral Band Data Using Mutual Information-Based Stochastic Gradient Descent Optimization

Abstract Views :248 | PDF Views:96

Authors

S. Manthira Moorthi ¹, D. Dhar ¹, R. Sivakumar ²

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

Source

Current Science, Vol 113, No 05 (2017), Pagination: 877-888

Abstract

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.

Full Text

References

ISRO, Resourcesat-2 Data User’s Handbook, NRSA Report No. NRSC: SDAPSA: NDCNDC: DEC11-364, 2011, Hyderabad, India.

Manthira, M. S., Kayal, R., Ramakrishnan, R. and Srivastava, P. K., RESOURCESAT-1 LISS-4 MX bands on ground co-registration by in-flight calibration and attitude refinement. Int. J. Appl. Earth Obs. Geoinf., 2008, 10, 140–146.

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.

Brown, L. G., A survey of image registration techniques. ACM Comput. Surv., 1992, 24, 325–376.

Fonseca, L. M. G. and Manjunath, B. S., Registration techniques for multisensor remotely sensed imagery. Photogramm. Eng. Remote Sensing, 1996, 62, 1049–1056.

Zitova, B. and Flusser, J., Image registration methods: a survey. Image Vis. Comput., 2003, 21, 977–1000.

Maintz, J. B. A. and Viergever, M. A., A survey of medical image registration. Med. Image Anal., 1998, 2, 1–36.

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.

Maes, F., Collignon, A., Vandermeulen, D., Marchal, G. and Suetens, P., Multimodality image registration by maximization of mutual information. IEEE Trans. Med. Imaging, 1997, 16, 187–198.

Viola, P. and Wells III, W. M., Alignment by maximization of mutual information. Int. J. Comput. Vis., 1997, 24, 137–154.

Thevenaz, P. and Unser, M., A pyramid approach to sub-pixel image fusion based on mutual information. In Proc. IEEE Int. Conf. Image Processing, Lausanne, Switzerland, 1996, vol. 265–268, pp. 16–19.

Cole-Rhodes, A. A., Johnson, K. L., LeMoigne, J. and Zavorin, I., Multiresolution registration of remote sensing imagery by optimization of mutual information using a stochastic gradient, IEEE Trans. Image Process., 2003, 12, 1495–1511.

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.

Klein, S., Staring, M., Murphy, K., Viergever, M. A. and Pluim, J. P. W., Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imag., 2010, 29, 196–205.

Mattes, D., Haynor, D. R., Vesselle, H., Lewellen, T. K. and Eubank, W., PET–CT image registration in the chest using freeform deformations. IEEE Trans. Med. Imag., 2003, 22, 120–128.

Unser, M., Splines: a perfect fit for signal and image processing. IEEE Signal Process. Mag., 1999, 16, 22–38.

Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D. L. G., Leach, M. O. and Hawkes, D. J., Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imag., 1999, 18, 712–721.

Goshtasby, A. A., Registration of image with geometric distortion. IEEE Trans. Geosci. Remote Sensing, 1988, 26, 60–64.

Plakhov, A. and Cruz, P., A stochastic approximation algorithm with step size adaptation. J. Math. Sci., 2004, 120, 964–973.

Li, Q., Wang, G., Liu, J. and Chen, S., Robust scale-invariant feature matching for remote sensing image registration. IEEE Geosci. Remote Sensing Lett., 2009, 6, 287–291.

An Overview of AVIRIS-NG Airborne Hyperspectral Science Campaign Over India

Abstract Views :253 | PDF Views:85

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

Source

Current Science, Vol 116, No 7 (2019), Pagination: 1082-1088

Abstract

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.

Full Text

References

Bhattacharya, B. K. and Chattopadhyay, C., A multi-stage tracking for mustard rot disease combining surface meteorology and satellite remote sensing. Comput. Electron. Agric., 2013, 90, 35– 44.

Bhattacharya, S., Majumdar, T. J., Rajawat, A. S., Panigrahy, M. K. and Das, P. R., Utilization of Hyperion data over Dongargarh, India, for mapping altered/weathered and clay minerals along with field spectral measurements. Int. J. Remote Sensing, 2012, 33(17), 5438–5450.

Ramakrishnan, D. and Bharti, R., Hyperspectral remote sensing and geological applications. Curr. Sci., 2015, 108(5), 879–891.

Sahoo, R. N., Ray, S. S. and Manjunath, K. R., Hyperspectral remote sensing of agriculture. Curr. Sci., 2015, 108(5), 848–859.

Das, B. S., Sarathjith, M. C., Santra, P., Sahoo, R. N., Srivastava, R., Routray, A. and Ray, S. S., Hyperspectral remote sensing: opportunities, status and challenges for rapid soil assessment in India. Curr. Sci., 2015, 108(5), 860–868.

Ramakrishnan, D. and Sahoo, R. N., Network Programme on Imaging Spectroscopy and Applications (NISA): science plan and implementation strategy. Department of Science and Technology, Government of India, 2016.

Ajay Kumar, K., Thap, N. A. and Kuriakose, S. A., Advances in spaceborne hyperspectral imaging systems. Curr. Sci., 2015, 108(5), 826–832.

Green, R. O. et al., Imaging spectroscopy and the airborne visible/ infrared imaging spectrometer (AVIRIS). Remote Sensing Environ., 1998, 65(3), 227–248.

Green, R. O. et al., The Moon Mineralogy Mapper (M3) imaging spectrometer for lunar science: instrument description, calibration, on-orbit measurements, science data calibration and on-orbit validation. J. Geophys. Res.: Planets, 2012, 116(E10).

Anonymous, Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space, 2017–2027 Decadal Survey for Earth science and applications from space. The National Academies of Science, Engineering and Medicine (ISBN 978-0-30946757-5). The National Academies Press, Washington, DC, USA, 2017; doi:10.17226/24938.

Retrieval of Atmospheric Parameters and Data-Processing Algorithms forAVIRIS-NG Indian Campaign Data

Abstract Views :224 | PDF Views:89

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

Source

Current Science, Vol 116, No 7 (2019), Pagination: 1089-1100

Abstract

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.

Full Text

References

Vane, G., Green, R. O., Chrien, T. G., Enmark, H. T., Hansen, E. G. and Porter, W. M., The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Remote Sensing Environ., 1993, 44(2–3), 127–143; doi:10.1016/ S0034-4257 (93)90012-M.

Green, R. O. et al., Imaging spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Remote Sensing Environ., 1998, 65(3), 227–248; doi:10.1016/S0034-4257 (98)0064-9.

Hamlin, L., Green, R. O., Mouroulis, P., Eastwood, M., Wilson, D., Dudik, M. and Paine, C., Imaging spectrometer science measurements for terrestrial ecology: AVIRIS and new developments. In IEEE Aerospace Conference Proceeding, 2011, pp. 1–7; http://dx.doi.org/10.1109/AERO.2011.5747395.

Gao, B. C. and Goetz, A. F., Column atmospheric water vapor and vegetation liquid water retrievals from airborne imaging spectrometer data. J. Geophys. Res. – Atmos., 1990, 95(D4), 3549–3564.

Adler-Golden, S. M. et al., Atmospheric correction for short-wave spectral imagery based on MODTRAN4. SPIE Proc. Imag. Spectrom., 1999, 3753, 61–69.

Etheridge, D. M., Steele, L. P., Langenfelds, R. L., Francey, R. J., Barnola, J.-M. and Morgan, V. I., Natural and anthropogenic hanges in atmospheric CO2 over the last 1000 years from air in Antarctic ice and firn. J. Geophys. Res., 1996, 101, 4115–4128.

Leung, D. Y. C., Caramanna, G. and Maroto-Valer, M. M., An overview of current status of carbon dioxide capture and storage technologies. Renew. Sust. Energ. Rev., 2014, 39, 426–443.

Wunch, D. et al., Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) XCO2 measurements with TCCON. Atmos. Meas. Tech., 2017, 10, 2209–2238.

Bovensmann, H., Burrows, J. P., Buchwitz, M., Frerick, J., Noel, S. and Rozanov, V. V., SCIAMACHY: mission objectives and measurement modes. J. Atmos. Sci., 1999, 5, 127–150.

Hamazaki, T., Kaneko, Y. and Kuze, A., Carbon dioxide monitoring from the GOSAT satellite. In Proceedings of XX ISPRS Conference, Istanbul, Turkey, 12–13 July 2004, pp. 3–5.

Frankenberg, C. et al., The Orbiting Carbon Observatory (OCO2): spectrometer performance evaluation using pre-launch direct sun measurements. Atmos. Meas. Tech., 2015, 8, 301–313.

Tadic, J. M. et al., A comparison of in situ aircraft measurements of carbon dioxide and methane to GOSAT data measured over railroad Valley Playa, Nevada, USA. IEEE Trans. Geosci. Remote Sensing, 2014, 52, 7764–7774.

Green, R. O., Measuring the spectral expression of carbon dioxide in the solar reflected spectrum with AVIRIS. In Proceedings of the 11th Annual Airborne Earth Science Workshop, Jet Propulsion Laboratory, Pasadena, CA, USA, 2001.

Dennison, P. E. et al., High spatial resolution mapping of elevated atmospheric carbon dioxide using airborne imaging spectroscopy: radiative transfer modeling and power plant plume detection. Remote Sensing Environ., 2013, 139, 116–129.

Thorpe, A. K. et al., Airborne DOAS retrievals of methane, carbon dioxide, and water vapor concentrations at high spatial resolution: application to AVIRIS-NG. Atmos. Meas. Tech., 2017, 10, 3833–3850.

Kotchenova, S. Y., Vermote, E. F., Matarrese, R. and Klemm Jr, F. J., Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part I: Path radiance. Appl. Opt., 2006, 45(26), 6762.

Lenoble, J., Radiative Transfer in Scattering and Absorbing Atmospheres: Standard Computational Procedures, A. Deepak Publishing, Hampton, VA.

Tanre, D. et al., Simulation of the Satellite Signal in the Solar Spectrum (5S), User’s Guide, Laboratoire d’Optique Atmospherique, U.S.T. de Lille, 59655 Villeneuve dáscq, France, 1986.

Vermote, E. F., Tanre, D., Deuze, J. L., Herman, M. and Morcrette, J. J., Second simulation of the satellite signal in the solar spectrum, 6S: an overview. IEEE Trans. Geosci. Remote Sensing, 1997, 35(3), 675.

Kaufman, Y. J., Tanre, D. and Boucher, O., A satellite view of aerosols in the climate system. Nature, 2002, 419, 215–223.

Kaufman, Y. J. et al., Relationship between surface reflectance in the visible and mid-IR used in MODIS aerosol algorithm – theory. Geophys. Res. Lett., 2002, 29(23), 31-1–31-4.

Kaufman, Y. J. et al., The MODIS 2.1 μm channel – correlation with visible reflectance for use in remote sensing of aerosol. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(5), 1286–1298.

Hsu, N. C. et al., Enhanced deep-blue aerosol retrieval algorithm: the second generation. J. Geophys. Res.: Atmos., 2013, 118, 9296– 9315; https://doi.org/10.1002/jgrd.50712.

Mishra, M. K., Retrieval of aerosol optical depth from INSAT-3D imager over Asian landmass and adjoining ocean: retrieval uncertainty and validation. J. Geophys. Res.: Atmosp., 2018, 123; https://doi.org/10.1029/2017JD028116.

Bruegge, C. J. et al., In situ atmospheric water-vapor retrieval in support of AVIRIS validation. Proc. SPIE, 1990, 1298, 150– 163.

Fowle F. E., The spectroscopic determination of aqueous vapour. Astrophys. J., 1912, 35, 149–162.

Frouin, R., Deschamps, Pierre-Yves and Lecomte, P., Determination from space of atmospheric total water vapor amounts by differential absorption near 940 nm: theory and airborne verification. J. Appl. Meteorol., 1990, 29, 448–460.

Berk, A., Bernstein, L. S. and Robertson, D. C., MODTRAN: a moderate resolution model for LOWTRAN7. GL-TR-89-0122. Air Force Geophysical Laboratory, Hanscom AFB, MA, 1989, p. 38.

Gordon, H. R., Brown, J. W. and Evans, R. H., Exact Rayleigh scattering calculations for use with the Nimbus 7 coastal zone color scanner. Appl. Opt., 1988, 27, 862–871.

Gordon, H. R., Atmospheric correction of ocean color imagery in the Earth Observing System era. J. Geophys. Res., 1997, 102, 17081–17106.

Username
Password
Remember me