Temporal Hyperspectral Representation Learning for Biotechnology
Self-supervised temporal hyperspectral representation learning for biotechnology.
Earth observation & applied AI
Amir
Hassanzadeh.
Understanding our world through remote sensing, machine learning, and physical simulation.
Research Assistant Professor
Rochester Institute of Technology
Research & software
Selected projects
From learning representations of Earth to simulating the sensors that observe it.
Scene Constructor: Large-Scale DIRSIG Scene Simulation
Automated scene construction for Landsat-scale simulations and sensor trade studies.
Landsat Split-Window Surface Temperature Uncertainty Framework
Estimating atmospheric water vapor and propagating uncertainty in Landsat split-window surface-temperature products.
3D Point Cloud Voxel Content Estimation in Simulated Forests
Estimating mixed material percentages inside forest voxels from 3D position and LiDAR intensity.
SPLASH: Multimodal Drone Hyperspectral Fusion
Aligning VNIR and SWIR pushbroom imagery from separate drones through global, row-wise, and elastic correction.
Snap Bean Yield Modeling and Harvest Scheduling
Connecting greenhouse spectroscopy with drone-based field observations for yield prediction and crop maturity assessment.
Jostar: Open-Source Feature Selection Library
Nine feature-selection algorithms for regression and classification, with a familiar Scikit-Learn-style interface.
Papers & collaboration
Publications
- 2026
Deep Imbalanced Multi-Target Regression: 3D Point Cloud Voxel Content Estimation in Simulated Forests ↗
IEEE Transactions on Geoscience and Remote Sensing
- 2026
Development of an Uncertainty Workflow to Support Landsat TIRS Split Window-Derived Surface Temperature Products ↗
Remote Sensing of Environment
- 2025
- 2025
Enhancing snap bean yield prediction through synergistic integration of UAS-Based LiDAR and multispectral imagery
Computers and Electronics in Agriculture · 230, 109923
- 2023
Forecasting Table Beet Root Yield Using Spectral and Textural Features from Hyperspectral UAS Imagery
Remote Sensing · 15(3), 794
- 2022
Evaluation of Leaf Area Index (LAI) of Broadacre Crops Using UAS-Based LiDAR Point Clouds and Multispectral Imagery
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 15, 4027–4044
- 2022
On the Use of Imaging Spectroscopy from Unmanned Aerial Systems (UAS) to Model Yield and Assess Growth Stages of a Broadacre Crop
Ph.D. dissertation · Rochester Institute of Technology
- 2021
Toward Crop Maturity Assessment via UAS-Based Imaging Spectroscopy—A Snap Bean Pod Size Classification Field Study ↗
IEEE Transactions on Geoscience and Remote Sensing · 60, 1–17
- 2021
Comparison of UAS-Based Structure-from-Motion and LiDAR for Structural Characterization of Short Broadacre Crops ↗
Remote Sensing · 13(19), 3975
- 2021
- 2020
Growth Stage Classification and Harvest Scheduling of Snap Bean Using Hyperspectral Sensing: A Greenhouse Study ↗
Remote Sensing · 12(22), 3809
- 2020
Yield modeling of snap bean based on hyperspectral sensing: a greenhouse study ↗
Journal of Applied Remote Sensing · 14(2), 024519
Amir Hassanzadeh is a Research Assistant Professor at Rochester Institute of Technology, working at the intersection of remote sensing, machine learning, and physics-based simulation. His work develops methods and software for extracting meaningful information from satellite, UAV, hyperspectral, thermal, and LiDAR observations.
His research spans self-supervised geospatial foundation models, Landsat surface-temperature retrieval and uncertainty, large-scale DIRSIG scene construction, and agricultural monitoring. His early research translated insights from controlled greenhouse spectroscopy to field-scale UAV remote sensing for crop-yield and harvest-maturity assessment.
He teaches Applications of Machine Learning in Remote Sensing and advises graduate and undergraduate researchers.
- Research Assistant Professor · RITApril 2026–Present
- Researcher / Engineer II · RITJune 2022–April 2026
- Ph.D. in Imaging Science · RIT2022
Get in touch
Research, collaboration, and opportunities.