The Introduction to Spectral Remote Sensing provides a foundational understanding of spectral analysis, covering electromagnetic spectrum principles, sensor types, and data processing techniques using ENVI software. The 10 modules are designed for entry-level analysts who want to focus on extracting information from spectral data for applications like vegetation and material identification.
Core Components:
- Fundamentals of Spectral Analysis: Physical characteristics of remotely sensed objects, data processing levels, data errors, and interactions between the electromagnetic spectrum and Earth’s surface.
- Understanding Atmospheric Effects: Atmospheric processes that affect the interpretation of satellite imagery, as well as calibration and correction techniques.
- Image Classification: Assign image pixels with similar properties to specific classes, which represent different features on the Earth’s surface.
- Hyperspectral Data Handling: Preparing data for hyperspectral analysis, working with spectral libraries, and detecting selected materials.
- Change Detection and Time-Series: Qualitative and quantitative information about changes that occur with natural events or human activities.
Learning outcomes
After this course, you will be able to:
- Interpret spectral information across the electromagnetic spectrum using appropriate sensors, resolutions, and color composites.
- Identify and select suitable remote sensing data sources based on mission objectives and area-of-interest coverage.
- Prepare and evaluate imagery through registration, mosaicking, calibration, and atmospheric correction.
- Assess image quality and mitigate common sources of error in remote sensing analysis.
- Apply spectral data and indices across different application domains, including thermal analysis.
- Perform image classification, change detection, and time-series analysis to assess landscape dynamics.