Chhapariya, K., Benoit, A., K. M. Buddhiraju, Kumar, A. (2024) ‘A Multitask Deep Learning Model for Classification and Regression of Hyperspectral Images: Application to the large-scale dataset,’ arXiv preprint. https://doi.org/10.48550/arXiv.2407.16384
Chhapariya, K., K. M. Buddhiraju, Kumar, A. (2024) ‘A Deep Spectral-Spatial Residual Attention Network for Hyperspectral Image Classification,’ IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/JSTARS.2024.3355071
Chhapariya, K., K. M. Buddhiraju, Kumar, A. (2022) ‘CNN-based Salient Object Detection on Hyperspectral Images using Extended Morphology’, Geoscience and Remote Sensing Letters. 10.1109/LGRS.2022.3220601
Chhapariya, K., K. M. Buddhiraju, Kumar, A. (2022) ‘CNN-based Salient Object Detection on Hyperspectral Images using Extended Morphology’, TechRxiv. Preprint. https://doi.org/10.36227/techrxiv.19422101.v1
Chhapariya, K., Kumar, A. & Upadhyay, P (2021) ‘A fuzzy machine learning approach for identification of paddy stubble burnt fields’, Spat. Inf. Res. 29, 319–329 https://doi.org/10.1007/s41324-020-00356-4
Chhapariya, K., Kumar, A. & Upadhyay, P (2021) ‘Kernel-Based MPCM Algorithm with Spatial Constraints and Local Contextual Information for Mapping Paddy Burnt Fields’. J Indian Soc Remote Sens 49, 1743–1754 https://doi.org/10.1007/s12524-021-01346-1
Chhapariya, K., Kumar, A. & Upadhyay, P (2020) ‘Handling non-linearity between classes using spectral and spatial information with kernel-based modified possibilistic c-means classifier’, Geocarto International, https://doi.org/10.1080/10106049.2020.1797186
Chhapariya, K., Benoit, A., Buddhiraju, K. M., & Kumar, A. (2024) 'A Deep Learning-Based Multitasking Model for Hyperspectral Image Analysis using Novel TAIGA Dataset,' Geoscience and Remote Sensing Symposium (IGARSS), IEEE International, doi: 10.1109/IGARSS53475.2024.10641762
Chhapariya, K., K. M. Buddhiraju, Kumar, A. (2023) ‘A Shuffled Dilated Convolutional Neural Network for Hyperspectral Image using Transfer Learning’, Geoscience and Remote Sensing Symposium (IGARSS), IEEE International. 10.1109/IGARSS52108.2023.10283327
Chhapariya, K., Ientilucci, E.J., Buddhiraju, K.M. and Kumar, A., (2023) ‘Hyperspectral Image Classification using Deep Learning Networks,’ In AGU Fall Meeting Abstracts. Link
Chhapariya, K., Ientilucci, E.J., Buddhiraju, K.M. and Kumar, A., (2023) ‘A Spectral-Spatial Classification Network for Hyperspectral Images using a Residual Attention Network,’ 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) https://doi.org/10.1109/WHISPERS61460.2023.10430700
Chhapariya, K., K. M. Buddhiraju, Kumar, A. (2022) ‘Spectral-spatial classification of hyperspectral images with multi-level CNN,’ 12th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). https://doi.org/10.1109/WHISPERS56178.2022.9955063
Chhapariya, K., K. M. Buddhiraju, Kumar, A. (2022) ‘Hyperspectral salient object detection using extended morphology with CNN’, Geoscience and Remote Sensing Symposium (IGARSS), IEEE International, doi: 10.1109/IGARSS46834.2022.9883107
Chhapariya, K., Kumar, A. (2019) ‘Effects of Untrained Classes on Kernel Based Modified Possibilistic C-Mean Classifier’ ISG-ISRS National Symposium, Innovations in Geospatial Technology for Sustainable Development with special emphasis on NER
Chapariya, K., Vashishtha, A. & Pawar, S. (2019, August). ‘Real-time data acquisition and visualization from an open government data platform through API’, 4th ISSE National Conference INAC.