Cloud Removal from Satellite Imagery
Deep learning architectures for SAR-optical fusion to reconstruct cloud-free Sentinel-2 images
Department of Computer Science & Engineering National Institute of Technology Puducherry
With over 15 years in academia, I lead research at the intersection of deep learning and remote sensing. My work focuses on developing intelligent systems that can interpret satellite imagery, enabling applications from urban planning to environmental monitoring.
As Head of the CSE Department at NIT Puducherry, I'm committed to nurturing the next generation of researchers while advancing our understanding of spatial data through innovative AI techniques.
Research Funding
Spatial Data Mining
Data Mining & ML
Deep learning architectures for SAR-optical fusion to reconstruct cloud-free Sentinel-2 images
Hybrid deep learning models combining 3D-CNN with attention mechanisms for precise land cover mapping
Co-location pattern discovery algorithms using Delaunay diagrams for geographic knowledge extraction
Multi-institutional collaborative project developing advanced data science methodologies for complex real-world applications
AI-based automated classification of satellite imagery using deep convolutional neural networks
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Q1Remote Sensing (MDPI)
Q1Journal of Real Time Image Processing
Q2Open to research collaborations, industry partnerships, and mentoring motivated students in AI and remote sensing