Geospatial Sensing and Intelligence Lab

ENGO 697: Retmote Sensing Systems and Advanced Analytics

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Quantitative and physical aspects of remote sensing systems, how to solve remote sensing inverse problems, how to integrate knowledge-driven quantitative remote sensing models with data-driven AI models, environmental monitoring applications

Semesters Taught: Fall 2024.

Course Information: Syllabus

#	Lecture
1	Course overview
2	What are key components in remote sensing systems
3	How to quantify radiation and radiative transfer process
4	SAR, hyperspectral, passive microwave and LiDAR systems
5	How to develop radiative transfer models in hypers/multi-spectral systems
6	How to develop radiative transfer models in SAR systems
7	How to solve inverse problems in remote sensing systems
8	How to solve key hyper-/multi-spectral radiative transfer models (RTM)
9	How does deep learning fit into remote sensing systems and fundamental concepts
10	CNN, Transformer, Graph neural networks, and Neural Radiative Field (NeRF)
11	How to Integrate Knowledge (e.g., Physical and Geometric models) into Deep Learning Models
12	Hyperspectral image classification and spectral unmixing