0000-0001-9022-9199 About
Unmesh Khati received the B.E. degree in electronics engineering from Nagpur University, Nagpur, India, in 2011, the M.Tech degree in remote sensing and GIS under joint education program from IIRS, ISRO, Dehradun, and Andhra University, India, in 2014, the Doctoral degree in SAR remote sensing from the Indian Institute of Technology Bombay, Mumbai, India, in 2019. He is an Assistant Professor in the Department of Astronomy Astrophysics and Space Engineering, Indian Institute of Technology Indore, India since December 2021. He was a Raman–Charpak Fellow with IETR, University of Rennes 1, Rennes, France, from January to June 2016. He was a Fulbright-Nehru Doctoral Visiting Researcher with the Jet Propulsion Laboratory, CalTech, Pasadena, CA, USA, from September 2017 to June 2018. He was a postdoctoral researcher at the NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, from December 2019 to December 2021. His research interests include applications of SAR remote sensing techniques for vegetation parameter retrieval, PolInSAR, and TomoSAR techniques for forest structure estimation.
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Publications (19)
- Integrating Dual-pol SAR parameters and Multi-spectral vegetation indices for Wheat crop classification in fragmented land parcels Save
- Physics Driven Machine Learning Based Multi-layer Surface Study of Faustini Crater Region Near the Lunar South Pole Using Chandrayaan-2 Dual Frequency Synthetic Aperture Radar (DFSAR) Save
- Activities of Indian Institute of Technology Indore IEEE GRSS Student Chapter (2023–2024): Recipient of the 2024 GRSS Student Chapter Excellence Award [Chapters] Save
- Detection of Soybean Pod Formation Stage Using Sentinel-1 SAR Data Save
- EOS-04 and S-1 for Mapping the Deforestation in Sub-Himalayan Forests in India Save
- Automated Stock Volume Estimation Using UAV-RGB Imagery Save
- Adapting CuSUM Algorithm for Site-Specific Forest Conditions to Detect Tropical Deforestation Save
- Gaussian process regression-based forest above ground biomass retrieval from simulated L-band NISAR data Save
- Model-Based Retrieval of Forest Parameters From Sentinel-1 Coherence and Backscatter Time Series Save
- Assessment of Forest Biomass Estimation from Dry and Wet SAR Acquisitions Collected during the 2019 UAVSAR AM-PM Campaign in Southeastern United States Save