RD
Dr. D Rajesh Reddy
Advanced Data Processing Research Institute, ECIL, JNTU College of Engineering, Jawaharlal Nehru Technological University, Chaitanya Bharathi Institute of Technology
0000-0002-2980-9175
About
Rajeshreddy Datla received B.Tech. (2002) and M.Tech. (2012) in CSE from JNTU Hyderabad, India. He has completed his PhD (CSE) at IIT Hyderabad (India) in 2021. He is currently working as Scientist/Engineer-SF in Advanced Data Processing Research Institute (ADRIN), Department of Space, Secunderabad, India. His research interests include remote sensing imagery analysis, pattern recognition, machine learning, and deep learning.
Employment
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Advanced Data Processing Research Institute research2007 - Present
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ECIL Executive engineer2006 - 2007
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Chaitanya Bharathi Institute of Technology Teaching2002 - 2006
Education
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JNTU College of Engineering MTech(CS)2009 - 2012
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Jawaharlal Nehru Technological University B.Tech1998 - 2002
Projects & Funding
Projects & funding information is unavailable.
Publications (24)
- CPL-PL: Contrapositive Learning-Based Pseudo-Labeling for Semi-Supervised Scene Classification in Remote Sensing Images Save
- SuperCLIP: Semantic Attribute-Guided Transformer With Super-Resolution and CLIP for Zero-Shot Remote Sensing Scene Classification Save
- TransRefine: Transformer-augmented feature refinement for zero-shot scene classification in remote sensing images Save
- EGANet: Elevation-guided attention network for scene classification in panchromatic remote sensing images Save
- An effective word embedding for Generalized Zero-Shot Scene Classification in Remote Sensing Images Save
- Federated learning to categorize air quality using satellite images Save
- A Hybrid Embedding for Generalized Zero-Shot Scene Classification in Remote Sensing Images Save
- Learning scene-vectors for remote sensing image scene classification Save
- RSZero-CSAT: Zero-Shot Scene Classification in Remote Sensing Imagery using a Cross Semantic Attribute-guided Transformer Save
- M2-APNet: A multimodal deep learning network to predict major air pollutants from temporal satellite images Save