| Estimation of vegetable crop parameter by multi-temporal UAV-borne images T Moeckel, S Dayananda, RR Nidamanuri, S Nautiyal, N Hanumaiah, ... Remote Sensing 10 (5), 805, 2018 | 99 | 2018 |
| Normalized Spectral Similarity Score () as an Efficient Spectral Library Searching Method for Hyperspectral Image Classification RR Nidamanuri, B Zbell IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2010 | 80 | 2010 |
| Segmentation based building detection approach from LiDAR point cloud AM Ramiya, RR Nidamanuri, R Krishnan The Egyptian Journal of Remote Sensing and Space Science 20 (1), 71-77, 2017 | 65 | 2017 |
| Use of field reflectance data for crop mapping using airborne hyperspectral image RR Nidamanuri, B Zbell ISPRS Journal of photogrammetry and remote sensing 66 (5), 683-691, 2011 | 52 | 2011 |
| Dynamic ensemble selection approach for hyperspectral image classification with joint spectral and spatial information BB Damodaran, RR Nidamanuri, Y Tarabalka IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2015 | 50 | 2015 |
| Dynamic linear classifier system for hyperspectral image classification for land cover mapping BB Damodaran, RR Nidamanuri IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2014 | 50 | 2014 |
| Spectral material mapping using hyperspectral imagery: a review of spectral matching and library search methods S Vishnu, RR Nidamanuri, R Bremananth Geocarto international 28 (2), 171-190, 2013 | 45 | 2013 |
| Object-level classification of vegetable crops in 3D LiDAR point cloud using deep learning convolutional neural networks R Jayakumari, RR Nidamanuri, AM Ramiya Precision Agriculture 22 (5), 1617-1633, 2021 | 44 | 2021 |
| A supervoxel-based spectro-spatial approach for 3D urban point cloud labelling AM Ramiya, RR Nidamanuri, K Ramakrishnan International Journal of Remote Sensing 37 (17), 4172-4200, 2016 | 41 | 2016 |
| Assessment of the impact of dimensionality reduction methods on information classes and classifiers for hyperspectral image classification by multiple classifier system BB Damodaran, RR Nidamanuri Advances in Space Research 53 (12), 1720-1734, 2014 | 37 | 2014 |
| Multi-scale dilated residual convolutional neural network for hyperspectral image classification K Pooja, RR Nidamanuri, D Mishra 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution …, 2019 | 35 | 2019 |
| Terrestrial laser scanner based 3D reconstruction of trees and retrieval of leaf area index in a forest environment I Indirabai, MVH Nair, RN Jaishanker, RR Nidamanuri Ecological Informatics 53, 100986, 2019 | 34 | 2019 |
| Object-oriented semantic labelling of spectral–spatial LiDAR point cloud for urban land cover classification and buildings detection AM Ramiya, RR Nidamanuri, R Krishnan Geocarto international 31 (2), 121-139, 2016 | 32 | 2016 |
| Seasonal variations in phenology and productivity of a tropical dry deciduous forest from MODIS and Hyperion B Christian, N Joshi, M Saini, N Mehta, S Goroshi, RR Nidamanuri, ... Agricultural and Forest Meteorology 214, 91-105, 2015 | 30 | 2015 |
| Transferring spectral libraries of canopy reflectance for crop classification using hyperspectral remote sensing data RR Nidamanuri, B Zbell biosystems engineering 110 (3), 231-246, 2011 | 30 | 2011 |
| Individual tree detection from airborne laser scanning data based on supervoxels and local convexity AM Ramiya, RR Nidamanuri, R Krishnan Remote Sensing Applications: Society and Environment 15, 100242, 2019 | 26 | 2019 |
| Semantic labelling of urban point cloud data AM Ramiya, RR Nidamanuri, R Krishnan The International Archives of the Photogrammetry, Remote Sensing and Spatial …, 2014 | 22 | 2014 |
| Direct estimation of leaf area index of tropical forests using LiDAR point cloud I Indirabai, MVH Nair, JR Nair, RR Nidamanuri Remote Sensing Applications: Society and Environment 18, 100295, 2020 | 21 | 2020 |
| Spectral identification of materials by reflectance spectral library search RR Nidamanuri, AM Ramiya Geocarto International 29 (6), 609-624, 2014 | 21 | 2014 |
| Hyperspectral discrimination of tea plant varieties using machine learning, and spectral matching methods RR Nidamanuri Remote Sensing Applications: Society and Environment 19, 100350, 2020 | 20 | 2020 |
