- 8:18 PM, Tuesday, 02 Mar 2021
|
|
|||||||||||||||||
Quantitative methods in remote sensing, satellite data based bio-geophysical parameters retrieval, remote sensing & applications, digital image processing, pattern recognition, hyperspectral image processing and analysis, microwave remote sensing, and LiDAR remote sensing.
In 2021
Reji, J, Nidamanuri, R.R., Ramiya, Astor, T., Wachendorf, M. and Buerkert, A. (2021). Multi-temporal estimation of vegetable crop biophysical parameters with varied nitrogen fertilization using terrestrial laser scanning, Computers and Electronics in Agriculture (in press).
Nautiyal, S., Goswami, M., Nidamanuri, R.R. et al. Structure and composition of field margin vegetation in the rural-urban interface of Bengaluru, India: a case study on an unexplored dimension of agroecosystems. Environ Monit Assess 192, 520 (2020). https://doi.org/10.1007/s10661-020-08428-6
1. Jha, S.S. and Nidamanuri R.R. (2020). Gudalur Spectral Target Detection (GST-D): A new benchmark dataset and engineered material target detection in multi-platform remote sensing data, Remote Sensing, 12(13), 2145. https://doi.org/10.3390/rs12132145
2. Nidamanuri, R. R. (2020). Hyperspectral discrimination of tea plant varieties using machine learning and spectral matching methods, Remote Sensing Applications: Society and Environment, 19, pp.100350 https://doi.org/10.1016/j.rsase.2020.10035
3. Dubacharla, G. and Nidamanuri, R.R. (2020). A novel supervised cascaded classifier system (SC2S) for robust remote sensing image classification, IEEE Geoscience and Remote Sensing Letters (press).
4. Jha S.S., Nidamanuri R.R. (2020). Dynamics of target detection using drone based hyperspectral imagery. In: Jain K., Khoshelham K., Zhu X., Tiwari A. (Eds). Lecture Notes in Civil Engineering, Vol 51. Springer, https://doi.org/10.1007/978-3-030-37393-1_10
5. Indirabai, I., Nair, M.V.H, Nair, J.R. and Nidamanuri, R.R. (2020). Direct estimation of leaf area index of tropical forests using LiDAR point cloud, Remote Sensing Applications: Society and Environment (press).
6. Dubacharla, G. and Nidamanuri, R.R. (2020). A real-time FPGA accelerated stream processing for hyperspectral image classification, Geocarto International, https://doi.org/10.1080/10106049.2020.1713231
7. Nair, S., Pillutla, R.C. and Nidamanuri, R.R. (2019). Semi-Empirical Model for Upscaling Leaf Spectra (SEMULS): A novel approach for modeling canopy spectra from in situ leaf reflectance spectra, Geocarto International. https://doi.org/10.1080/10106049.2019.1665716
8. Rajeswari B., Srivalsan N., Nidamanuri R.R., and Gorthi RKSS (2018), Batch-mode active learning-based superpixel library generation for very high-resolution aerial image classification. In: Verma, N.K. and Ghosh, A.K. (Eds), Computational Intelligence: Theories, Applications and Future Directions - Volume II (Advances in Intelligent Systems and Computing book series (AISC, volume 799), 307-318. https://doi.org/10.1007/978-981-13-1135-2_24
9. Madalasa, P. Gorthi R.K.S.S, Martha,T.R., Nidamanuri, R.R. and Mishra, D. (2018). Bayesian approach for landslide identification from high-resolution satellite images. In: Chaudhuri, B.B., Kankanhalli, M.S. & Raman, B (Eds), Advances in Intelligent Systems and Computing 704, https://doi.org/10.1007/978-981-10-7898-9_2
10. Rajeswari B., Gorthi RKSS and Nidamanuri R.R. (2018). Learning-based fuzzy fusion of multiple classifiers for object-oriented classification of high resolution images. In: Chaudhuri, B.B., Kankanhalli, M.S. and Raman, B (Eds) Advances in Intelligent Systems and Computing book series (AISC, volume 703), 63 – 78. https://doi.org/10.1007/978-981-10-7895-8
11. Jha S.S., Manohar, C.V.S.S. and Nidamanuri, R.R. (2019). Flexible atmospheric compensation technique (FACT): a 6S based atmospheric correction scheme for remote sensing data, Geocarto International, https://doi.org/10.1080/10106049.2019.1588391
12. Ramiya A.M., Nidamanuri, R.R. and Krishnan, R. (2019) "Individual tree detection from airborne laser scanning data based on supervoxels and local convexity". Remote Sensing Applications: Society and Environment, 15, 100242
13. Supriya Dayananda, Thomas Astor, Jayan Wijesingha, Subbarayappa Chickadibburahalli Thimappa , Hanumanthappa Dimba Chowdappa, Mudalagiriyappa, Rama Rao Nidamanuri, Sunil Nautiyal and Michael Wachendorf (2019). Multi-temporal monsoon crop biomass estimation using hyperspectral imaging, Remote Sensing, 11(15), 1771.
14. Athira, K., Sooraj, N.P., Jaishanker, R., Saroj Kumar, V., Sajeev, C.R., Pillai, M.S., Govind, A., Ramarao, N., Dadhwal, V.K. (2019). Chromatic exclusivity hypothesis and the physical basis of floral color, Ecological Informatics, 49: 40 – 44.
15. Indirabai, I., Nair, M.V.H, Nair, J.R. and Nidamanuri, R.R. (2019). Terrestrial laser scanner based 3d reconstruction of trees and retrieval of leaf area index in a forest environment, Ecological Informatics, 53, 100986.
16. Indirabai, I., Nair, M.V.H, Nair, J.R. and Nidamanuri, R.R. (2019). Estimation of forest structural attributes using ICESat/GLAS-spaceborne laser altimetry data in the Western Ghats Region of India, Journal of Geovisualization and Spatial Analysis (2019) 3:10, https://doi.org /10.1007/s41651-019-0033-2
17. Thomas Moeckel, Supriya Dayananda, Rama Rao Nidamanuri, Sunil Nautiyal, Nagaraju Hanumaiah , Andreas Buerkert, and Michael Wachendorf (2018). Estimation of Vegetable Crop Parameter by Multi-temporal UAV-Borne Images, Remote Sens. 2018, 10, 805.
18. Rajeswari B., Srivalsan N., Nidamanuri R.R., and Gorthi RKSS (2018), Active learning-based optimized training library generation for object-oriented image classification, IEEE Transactions on Geoscience and Remote Sensing, 56, 575 - 585.
19. Ramiya A.M., Nidamanuri, R.R. and Krishnan, R. (2019), Assessment of various parameters on 3D semantic object-based point cloud labelling on urban LiDAR dataset, Geocarto International, 34, 817-838.
20. Ramiya A.M., Nidamanuri, R.R. and Krishnan, R. (2016). Supervoxels based spatial-spectral approach for 3D object based urban point cloud labelling, International Journal of Remote Sensing, 37, 1- 16.
21. Ramiya A.M., Nidamanuri, R.R. and Krishnan, R. (2017). Segmentation based building detection approach from LiDAR point cloud, The Egyptian Journal of Remote Sensing and Space Science, 20, 71 – 77.
22. Nidamanuri, R.R. (2016). Multiple spectral similarity metrics for surface materials identification using hyperspectral data, Geocarto International, 31, 845 – 859.
23. Dhanya S Pankaj and Nidamanuri R.R. (2016). A robust estimation technique for 3D point cloud registration, Image Analysis and Stereology 35, 15- 28.
24. Ramiya A.M., Nidamanuri, R.R. and Krishnan, R. (2016). Object-oriented semantic labelling of spectral–spatial LiDAR point cloud for urban land cover classification and buildings detection, Geocarto International, 31, 121 – 139.
25. Kadapala B.R., Ahamed, J.Hebbar, R. Raj, U. and Nidamanuri R.R. (2016). Object based classification techniques for citrus orchards, Journal of Geomatics, 10, 65 – 70
26. Christian, A., Joshi, N., Saini, M., Mehta, N.K, Goroshi, S., Nidamanuri R.R., Thenkabail, P., Desai, A.R., Nadiminti, K. (2015). Seasonal variations in GPP of a tropical deciduous forest from MODIS and Hyperion, Agricultural and Forest Meteorology, 214-215, 91-105.
27. Damodaran, B.B., and Nidamanuri R.R., and Tarabalka, Y. (2015). Dynamic ensemble selection approach for hyperspectral image classification with joint spectral and spatial information, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6), 2405 – 2417.
28. Venkatesh. S.S, Anil Kumar. A.K, Deepak Mishra, Nidamanuri R.R and Karthikesan. D (2015). Three dimensional motion parameter estimation from consecutive perspective views of rigid planar patch: algorithm based on pure parameters and singular value decomposition and implementation, International Journal of Applied Engineering Research, 10(8), 6122 – 6128.
29. Nidamanuri R.R and Damodaran, B.B. (2014): Dynamic linear classifier system for hyperspectral image classification for land cover mapping, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 2080 – 2093.
30. Damodaran, B.B., and Nidamanuri R.R (2014): Assessment of the impact of dimensionality reduction methods on information classes and classifiers for hyperspectral image classification by multiple classifier system. Advances in Space Research, 53, 1720–1734.
31. Ramiya, A. M., Nidamanuri R.R., and Krishnan, R.(2014). Semantic labelling of urban point cloud data, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 907-911, doi:10.5194/isprsarchives-XL-8-907-2014.
32. Nidamanuri R.R and Ramiya, A.M. (2014), Spectral identification of materials by reflectance library search, Geocarto International, 29, 609-624.
33. Sennaraj V., Nidamanuri R.R and Bremananth, R. (2012): Spectral material mapping using hyperspectral imagery: a review of spectral matching and library search methods, Geocarto International, 28, 171 – 190.
34. Nidamanuri R.R. and Zbell, B. (2013), Understanding the unique spectral signature of winter rape, Journal of the Indian Society of Remote Sensing, 41, 57 – 70.
35. Nautiyal, S. and Nidamanuri R.R (2012), Ecological and socioeconomic impacts of conservation policies in biodiversity hotspots: a case study from Rajiv Gandhi National Park, India, Journal of Environmental Studies and Sciences, 2, 165 – 177.
36. Nautiyal, S. and Nidamanuri R.R. (2012). Protected area management in biodiversity hotsopts: a case study from Nagarahole National Park, India in Land Management in Marginal Mountain Regions: Adaptation and Vulnerability to Global Change, Saxena, et al (Eds), Published by United Nations University, Japan.
37. Nidamanuri R.R and Zbell, B. (2011). Existence of characteristic spectral signatures for agricultural crops – potential for automated crop mapping by hyperspectral imaging, Geocarto International, 26, 524 - 533.
38. Nidamanuri R.R and Zbell, B. (2011). A spectral matching quality indicator for material mapping using spectral library search methods, International Journal of Remote Sensing, 32, 7151 - 7162.
39. Nidamanuri R.R. and Zbell, B. (2011). Transferring spectral libraries of canopy reflectances for crop classification using hyperspectral remote sensing data, Biosystems Engineering, 110, 231-246.
40. Nidamanuri R.R and Zbell, B. (2011). Use of field reflectance data for crop mapping using airborne hyperspectral image, ISPRS Journal of Photogrammetry and Remote Sensing, 66, 683 - 691.
41. Nidamanuri R.R and Zbell, B. (2011). Normalized Spectral Similarity Score (NS3) as an efficient spectral library searching method for hyperspectral image classification, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4, 226 - 240.
42. Nautiyal, S. and Nidamanuri R.R. (2010). Conserving biodiversity in protected area of biodiversity hotspot in India: a case study, International Journal of Ecology and Environmental Sciences, 36, 195 - 200.
43. Nidamanuri R.R. and Zbell, B. (2010). A method for selecting optimal spectral resolution and comparison metric for material mapping by spectral library search, Progress in Physical Geography, 34, 47 - 58.
44. Rao, N.R (2008). Development of a crop-specific spectral library and discrimination of various agricultural crop varieties using hyperspectral imagery, International Journal of Remote Sensing, 29, 131 - 144.
45. Rao, N.R., Garg, P.K, and Ghosh, S.K. (2006). Estimation and comparison of leaf area index of agricultural crops using IRS LISS-III and EO-1 Hyperion images, Journal of the Indian Society of Remote Sensing, 34, 69-78.
46. Rao, N.R., Garg, P.K, and Ghosh, S.K. (2006). Evaluation of radiometric resolution on the estimation of leaf area index of agricultural crops, GIScience & Remote Sensing. 43, 1-11.
47. Rao, N.R., Garg, P.K, and Ghosh, S.K. (2007). Evaluation of radiometric resolution on land use/land cover mapping in an agricultural area, International Journal of Remote Sensing, 28, 443-450.
48. Rao, N.R., Garg, P.K, and Ghosh, S.K. (2007). Estimation of plant chlorophyll and nitrogen concentration of agricultural crops using EO-1 Hyperion hyperspectral imagery, Journal of Agricultural Science, 146, 1-11.
49. Rao, N.R., Ghosh, S.K. and Garg P.K. (2007). Development of cultivar based crop spectral library and classification of crop cultivars using hyperspectral data, Precision Agriculture, 8, 173-185.
50. Rao, N.R., Sharma, N., Kapoor, M. and Venkateswarlu, K. (2007). Yield prediction and waterlogging assessment for tea plantation land using satellite image based techniques. International Journal of Remote Sensing, 28, 1561 - 1576.
51. Rao, N.R. and Saravanan, S. (2007). Remote sensing based estimation of agricultural crops damage due to Asian tsunami-A case study in part of Nagapattinam, India, International Journal of Applied Remote Sensing, 2, 51 - 632.
52. Rao, N.R., Garg, P.K, and Ghosh, S.K. (2007). Remote sensing based estimation of cotton canopy cover using linear mixture modelling approach, International Journal of Applied Remote Sensing, 1, 63- 71.
53. Rao, N.R., Garg, P.K, and Ghosh, S.K. (2007). Potential use of hyperspectral imagery to eliminate saturation effect of NDVI at high canopy density of vegetation, International Journal of Applied Remote Sensing, 1, 10- 21.
54. Rao, N.R. (2009). Conformity analysis of cotton crop using remote sensing and GIS, Geospatial World Magazine (09/01/2009).
55. Bhusan, D. B. and Nidamanuri, R.R. (2013). Dynamic classifier system for hyperspectral image classification, IEEE Xplore, Doi: 10.1109/IGARSS.2013.204353
Publications in academic conferences/symposia
56. Pooja, K., Nidamanuri, R.R., and Mishra, D. (2019). Multi-scale dilated residual convolutional neural network for hyperspectral image classification. 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 1-5.
57. Joseph, J., Mishra, D., Martha, T.R. and Nidamanuri, R.R. (2018). A deep learning framework for automatic landslide inventory mapping (DLF-ALM), INCA38: 38th INCA International Congress- Emerging Technologies in Cartography, Hyderabad, India
58. Ramiya, A. M., Nidamanuri, R.R. and Krishnan, R. (2014). Semantic labelling of urban LiDAR point cloud data. Proceedings of ISPRS TC VIII Mid-Term Symposium, Hyderabad.
59. Rajeswari B., Srivalsan N., Nidamanuri R.R., and Gorthi RKSS (2017). Batch mode active learning based superpixel library generation for very high resolution aerial image classification, International Conference on Computational Intelligence: Theories, Applications and Future Directions, Dec. 2017, IIT Kanpur, India.
60. Rajeswari B., Srivalsan N., Nidamanuri R.R., and Gorthi RKSS (2017). Learning based fuzzy fusion of multiple classifiers for object oriented classification of high resolution images, International Conference on Computer Vision and Image Processing, Sept. 2017, IIT Roorkee, India.
61. Veena, V.S., Mishra, D., Martha, T.R. and Nidamanuri, R.R. (2016). Automatic detection of landslides in object-based environment using open source tools, GEOBIA 2016, GEOBIA 2016: Solutions and Synergies, 14–16 September 2016, Enschede, The Netherlands.
62. Indirabai, I. and Nidamanuri, R.R. (2016). Three dimensional urban building detection using LiDAR data using point cloud library and visual studio C++, Proceedings of the Imaging & Geospatial Technology Forum (IGTF) 2016 American Society for Photogrammetry & Remote Sensing (ASPRS) Conference and Co-Located JACIE Workshop, 11-15 April 2016, Fort Worth, USA.
63. Pankaj, D.S and Nidamanuri, R.R. (2016). Robust multi-view registration of point clouds. International Conference on Communication Systems and Networks, (ComNet 2016), 21-23 July, 2016, Ahmedabad, India
64. Pankaj, D.S and Nidamanuri, R.R. (2016). ProLoSAC: An Improved RANSAC algorithm for pairwise registration of 3d point clouds. Indian Conference on Computer Vision Graphics and Image Processing, Dec. 2014, Bangalore.
65. Nidamanuri, R.R. (2014). Spectral discrimination of tea plant varieties by statistical, machine learning and spectral similarity methods, 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 24-27 June 2014, Switzerland.
66. Dhanya S Pankaj, Nidamanuri, R.R. and P Bhanu Prasad (2013). 3-D imaging techniques and review of products, Proceedings of the “International Conference on Innovations in Computer Science and Engineering”, Hyderabad.
67. Bhusan, D. B.and Nidamanuri, R.R. (2013). Dynamic classifier system for hyperspectral image classification, IGARSS 2013, Melbourne.
68. Bhusan, D. B.and Nidamanuri, R.R. (2012). Assessment of relationship between information classes, classifiers and dimensionality reduction methods for hyperspectral image classification by multiple classifier system, National Symposium on ‘Space Technology for Food & Environmental Security’ &Annual Convention of Indian Society of Remote Sensing, Dec. 5 -7, 2012, New Delhi.
69. Vishnu S., Nidamanuri , R.R. and Bremananth, R. (2012). Normalized spectral matching score (NSMS): an improved method for hyperspectral image classification, National Symposium on ‘Space Technology for Food & Environmental Security & Annual Convention of Indian Society of Remote Sensing’, Dec. 5 -7, 2012, New Delhi.
70. Bhusan, D. B.and Nidamanuri, R.R. (2012). Impact of feature reduction methods on target detection methods for hyperspectral image classification by multiple classifier system, presented in “39th COSPAR Scientific Assembly”, 14 – 22 July, 2012, Mysore.
71. Nidamanuri, R.R. and Saquib, Q. (2011). Integration of LIDAR and hyperspectral data for object based image classification, presented in "National Symposium on ‘Empowering Rural India through Space Technology" organized Indian Society of Remote Sensing, November 8 -11, 2011, Bhopal.
72. Nidamanuri, R.R. and Ramiya, A.M. (2011). Estimation of tropical forest biophysical parameters using near UV and NIR reflectance from GOSAT TANSO – CAI sensor, presented in "International Conference on "Adaptive Management of Ecosystems: The Knowledge Systems of Societies for Adaptation and Mitigation of Impacts of Climate Change" 19-21, October 2011, Bangalore.
73. Nidamanuri, R.R. and Zbell B. (2009). On the transferability of vegetation spectral library, presented in 33rd International Symposium on Remote Sensing of Environment, May 3 - 7, 2009, Stresa, Italy.
74. Rao, N.R. and Alex, S.H, (2007). A Spectral library approach for the identification of diseased mango trees, presented at International Symposium: "Application of Precision Agriculture for Fruits and Vegetables", held during January 6-9, 2008 at Orlando, Florida, USA.
75. Rao, N.R., Garg, P.K, and Ghosh, S.K. (2006). Development of variety based crops spectral library and discrimination of various crop species using hyperspectral remote sensing data, in proceedings of the International Conference on Geoinformatics-2006, held at Hyderabad during 2-5, June 2006.
76. Rao, N.R., Garg, P.K, and Ghosh, S.K. (2006). Evaluation of the impact of radiometric resolution on land use/land cover classification, in Proceedings of the Indo-Australian Conference on IT in Civil Engineering, held at Indian institute of Technology, Roorkee, India during 19-21 Feb, 2006.
77. Rao, N.R. and Ghosh, S.K., Rao, N.V. (2005). Land use/land cover classification with multispectral and hyperspectral EO-1 data: A comparison, presented in 31st International Symposium on Remote Sensing of Environment, held on June 20 - 24, 2005, St. Petersburg, Russia.
78. Rao, N.R. and Venkateswarlu, K. (2005). Remote sensing based estimation of agricultural crops damage due to Asian Tsunami, presented in National Seminar on GIS Applications in Rural Development with Focus on Disaster Management, held on 9-10, Mar 2005, National Institute of Rural Development, Hyderabad, India.
79. Rao, N.R., Garg, P.K, and Ghosh, S.K. (2004). Estimation of leaf area index of agricultural crops using remote sensing data, presented in International Conference on Geoinformatics-2004, held on December 12-14, 2004, University of Mysore, Mysore, India.
80. Rao, N.R., Garg P.K., and Jagadish M.V. (2003). Conformity analysis of cotton crop using remote sensing and GIS techniques, presented in 30th International Symposium on Remote Sensing of Environment, November 10 - 14, 2003, Honolulu, Hawaii, U.S.A.
Ongoing Research Projects
Indo-German consortium research project "The Rural-Urban Interface of Bangalore: A Space of Transitions in Agriculture, Economics, and Society"
Taking forward the goal of the MOU signed in October 2012 between The Deutsche Forschungsgemeinschaft (DFG), Germany and the Department of Biotechnology of India (DBT), Government of India for Scientific Cooperation, the following institutions have joined together to perform the theme specific scientific research, knowledge sharing and technical cooperation.
Indian institutions
The University of Agricultural Sciences, Bengaluru (coordinating institution)
National Institute of Animal Nutrition and Physiology (NIANP), Bengaluru
Indian Institute of Space Science and Technology (IIST), Thiruvanthapuram
Institute for Social and Economic Change (ISEC), Bengaluru
Institute of Wood Science and Technology (IWST), Bengaluru
Ashoka Trust for Research in Ecology and Environment (ATREE Bengaluru and
AjimPremji University (APU), Bengaluru
German institutions
University of Göttingen
University of Kassel
As part of the scientific goals of these consortia, IIST and ISEC-Bengaluru have got funding for a joint research project “Integrating air and space borne spectroscopy and laser scanning to assess structural and functional characteristics of crops and field margin vegetation”.
Funding agency: Department of Biotechnology, Govt. of India
Funding amount: Rs. 200 lakh
Duration: three years (16 Nov. 2016 – 15 Nov. 2019)
PI: Dr Rama Rao Nidamanuri
Co-PIs:
Prof. Sunil Nautiyal, (ISEC – Bengaluru)
Ms. Ramiya A M (IIST)
Development of a Stand-Alone Atmospheric Correction Module for Hyperspectral Data
This project is part of the DST’s national initiative on hyperspectral remote sensing, creating for infrastructure (equipment) and research grants to academic institutions in India. As part of this, IIST has taken up the responsibility of developing advanced algorithms based modules for atmospheric correction of hyperspectral data. Various scientific simulations, validations and extended remote sensing applications would be undertaken in this regard.
Funding agency: Department of Science and Technology (DST), Government of India
Budget: Rs. 142.49 lakh
Duration: three years (April 2016 - March 2019)
PI: Dr Rama Rao Nidamanuri
Spectral Biochemical Analysis of Forest Species using Hyperspectral Remote Sensing – A Case Study from Eastern Ghats Forest Ecosystems
Leaf chlorophyll and nitrogen are the basic indicators of vegetation health condition and are manifestations of the biogeochemical processes which provide information on whether an ecosystem sustains or not. The specific objectives of this research are:
1. estimation of canopy level chlorophyll and nitrogen content of various species using integrated field and satellite based methods, and 2. correlating spectral variations with that of canopy biochemical patterns under stress conditions.
Funding agency: Department of Science and Technology (DST), Government of India
Budget: Rs. 39.5 lakh
Duration: three years (April 2016 - March 2019)
PIs: Dr. Ramachandra Prasad, IIIT-Hyderabad, Dr Rama Rao Nidamanuri (IIST).
3. Above ground volume/biomass estimation and validation using airborne S- and L-band NISAR data and radiative transfer modeling
Funding agency: SAC-Ahmedabad
Budget: Rs. 19 lakh
Role: co-PI
PI: Dr. Smitha Ashok, All Saints College, Trivandrum.
4. City GML based 3D models for smart cities in India using LiDAR point cloud
Funding agency: Department of Science and Technology (DST), Government of India
Budget: Rs. 33 lakh
Role: co-PI
PI: Dr Ramiya A M
Duration: two years (Octiber 2020 - October 2022)
Doctoral theses supervised at IIST
Completed
Supervised four PhD theses in different areas of advanced hyperspectral imaging, and 3D image processing and point cloud
modelling.
Ongoing
1. Mr. Sudhanshu Sekhar Jha, Algorithms for complex target detection in hyperspectral images
2. Mr. Manohar Kumar, Knowledge based spectral unmixing in hyperspectral images
3. Mr. Gyaneshwar D, Parallel processing algorithms for hyperspectral imaging analysis
4. Ms. Reji J, Algorithms for 3D moldeiing and integration of LIDAR and hyperspectral data for agricultural crops monitoring
5. Mr. Abhijeeth Kumar, A study on structure, dynamics and microphysics of precipitation using X-band dual-polarization radar (jointly with Dr T Narayana Rao, NARL, Tirupati)
6. Mr. Deepak Singh Bisht, Studies on Dynamics of Integrated Water Vapor (IWV) and Precipitation Measurements from a GNSS Receiver Network, (jointly with Dr T Narayana Rao, NARL, Tirupati)
7. Mr. Jayasimha, Deep learning approaches for anomaly and target detection in hyperspectral imagery
8. Mr. Suraj Reddy, Studies on multi-source remote sensing data integration for modelling and estimation of forest biomass (jointly with Dr V K Dadhal, director, IIST)
A. A. DST Sponsored Central Facility for Hyperspectral Remote Sensing for Southern Region
A central lab facility with all the advanced equipment for researching on hyperspectral remote sensing/image processing
has been set up in the Department of Earth and Space Sciences, Indian Institute of Space Science and Technology. The following instruments available.
1. Hyperspectral spectroradiometer (400 - 2500nm)
2. Hyperspectral imaging spectroradiometer (400 - 1000nm)
3. Plant canopy analyser
4. Chlorophyll concentration meter
5. Quantum sensor
6. Laser distance meter (Leica Disto S-910)
Researchers and students from Andhra Pradesh, Karnataka, Kerala, Pondicherry, Tamil Nadu, and Telangana can access the facilities.
Contact: Dr. Rama Rao Nidamanuri
email: rao@iist.ac.in
B. Data Resources
As part of the research and teaching activities of the faculty, IIST has acquired/generated several remote sensing data of multispectral,
microwave and LiDAR remote sensing nature.
Recently IIST acquired good quality terrestrial laser scanner data on trees, crops, and buildings. We will be happy to share some
of the data for students’ projects. Interested may please contact (email: rao@iist.ac.in) explaining the purpose.
Apart, we can acquire ground / lab / outdoor 3D laser point cloud and hyperspectral images for a variety of applications.
Interested researchers / students may contact with the idea of joint works / usage / collaboration.
C. Benchmark Dataset for Target Detection Experiments
Advanced remote sensing techniques have been gaining popularity for decision level strategic applications such as military security and surveillance, and law enforcement.
Target detection in imagery is the simplest yet powerful general approach suitable for addressing surface object identification and monitoring
requirements in military, mineral recognizance, environmental monitoring and enforcement etc. Noticing the vast potential of target detection
using hyperspectral imagery and the lack of a reference dataset for researchers, we have brought out an exemplary dataset – multi-platform
(ground, airborne and space-borne) remote sensing imagery (hyperspectral) for target detection / engineered material detection studies.
This dataset is described in our recent article “Jha S.S. and Nidamanuri R.R. (2020). Gudalur Spectral Target Detection (GST-D): A new
benchmark dataset and engineered material target detection in multi-platform remote sensing data, Remote Sensing (Accepted)”
We will be happy to share the datasets with those interested and assist in the appropriate processing and analyses of imagery for various other perspectives.
Interested researchers may contact me.
Thank you!
NIR: Networking of Independent Researchers
A seamless association of researchers across the disciplines-age-position -institution is the evolving approach of research collaboration.
In the NIR, every member is aware and assured of a set of resources without out explicit costs associated with it.
Often, the research starts with a member proposing a topic for research - can come simply out of nowhere research proposal/PhD/masters
thesis etc., no limit.
The problem will be put for discussion and will be further guided for a team formation. The members in the team contribute to the
execution of the plans and put in skills, tools, expert suggestion, blessing supervision. The outcome will be credited based on the
level and specifics of member involvement.
The key aspects of NIR is to (i) optimize the utility, reachability and value creation of tools/techniques/equipment available across
different institutions, and (ii) to help anyone requiring technical guidance, handholding, research mentoring irrespective of the
existence or level of a formal relationship.
Proposing this approach, we will be pleased to interact and share our resources for research in a truly cross-discipline nature
and is independent of the level of researcher (anyone can approach with a relevant requirement/idea).
Topics of research are plenty – urban, water resources, water quality (marine/inland), forestry, agriculture,
soils, environmental monitoring and management, air, soil, and water pollution etc.
