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Introduction to Spectral Remote Sensing

Geospatial eLearning Program


The Introduction to Spectral Remote Sensing Learning Path is designed to expose learners to the common applications of spectral analysis. Each course utilizes example datasets to illustrate concepts using ENVI software.


Sections in This Course


Introduction to Spectral Interpretation

3 Hours

Explores discrete regions of the electromagnetic spectrum and their use in conducting spectral analysis.

  • Spectral sensors and bands
  • Different types of resolution
  • Color composites
  • Contrast-stretching techniques

Identifying Data Resources

3 Hours

Discusses several sources of public-domain remote sensing data.

  • Data portals
  • Different platforms used for Earth observation
  • Considerations for choosing imagery that best meets a mission objective

Evaluating Area of Interest Coverage

3 Hours

Explores the relationship between data points on the ground, data values in remote sensing images and common preprocessing techniques.

  • Image registration
  • Image-to-map registration
  • Image mosaic creation

Spectral Data Applications

3 Hours

Explores the applications of spectral data across various industries.

  • Spectral applications by industry
  • Spectral indices
  • Interpreting thermal imagery

Understanding Atmospheric Effects

3 Hours

Describes atmospheric processes that affect interpretation of satellite images.

  • Atmospheric absorption and scattering
  • Digital numbers
  • Calibration to radiance and reflectance
  • Empirical and model-based atmospheric correction techniques

Understanding Image Quality

3 Hours

Describes factors that need to be considered to properly assess the quality of remote sensing images.

  • Sources of error in remote sensing analysis
  • Data-processing levels
  • How to mitigate visual flaws in data

Image Classification Overview

3 Hours

Discusses the role of image classification in remote sensing.

  • Supervised and unsupervised classification
  • Post-classification accuracy assessment

Change Detection and Time Series

3 Hours

Describes methods used in remote sensing to assess changes to the Earth’s landscape over time.

  • Image differencing
  • Thematic change detection
  • Time series construction and temporal profiles

Working with Non-Spectral Data

3 Hours

Describes how non-spectral data can complement, and potentially improve, spectral analysis.

  • LiDAR elevation data
  • Vector data
  • Synthetic aperture radar (SAR) data

Introduction to Hyperspectral Analysis

3 Hours

Introduces hyperspectral analysis, also called imaging spectroscopy.

  • Hyperspectral data preparation
  • Working with spectral libraries
  • Target detection

This Course Includes:

  • Course Length: 30 Hours
  • ENVI Software: 60-day temp license and required modules
  • Learning Checks: Quiz at end of each lesson
  • Certificate: Certificate of Completion at course end

System Requirements

  • Operating System: Windows 10 or 11
  • Disk Space: Approx. 4GB for installation
  • Memory: Minimum 8GB

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