This course explores discrete regions of the electromagnetic spectrum and their use in conducting spectral analysis. Concepts include spectral sensors and bands, different types of resolution, color composites, and contrast-stretching techniques to enhance image features.

This course describes factors that need to be considered to properly assess the quality of remote sensing images. Concepts include sources of error in remote sensing analysis, data-processing levels, and how to mitigate visual flaws in data.

This course explores the relationship between data points on the ground and data values in remote sensing images. Concepts include image registration, image-to-map registration, and image mosaic creation. These preprocessing methods are often conducted prior to spectral analysis.

This course describes methods used in remote sensing to assess changes to the Earth’s landscape over time. Change detection and time-series analysis can provide qualitative and quantitative information about changes that occur with natural events or human activities. Concepts include image differencing, thematic change detection, time series construction, and temporal profiles.

This course describes several sources of public-domain remote sensing data. Concepts include data portals, different platforms used for Earth observation, and considerations for choosing imagery that best meets a mission objective.

This course describes the role of image classification in remote sensing. Concepts include supervised and unsupervised classification, and post-classification accuracy assessment.

This course provides an introduction to hyperspectral analysis, also called imaging spectroscopy. Hyperspectral images contain at least 40 spectral bands. Concepts include preparing data for hyperspectral analysis, working with spectral libraries, and using target detection to locate selected materials in an image based on their spectral properties.

This course describes atmospheric processes that affect interpretation of satellite images. Concepts include atmospheric absorption and scattering, digital numbers, calibration to radiance and reflectance, and empirical and model-based atmospheric correction techniques.

This course describes how non-spectral data can complement, and potentially improve, spectral analysis. Concepts include gridded and LiDAR elevation data, vector data, and synthetic aperture radar (SAR) data.

This course explores the applications of spectral data across various industries. It details the interactions between the electromagnetic spectrum and its effects on the Earth’s surface.

This 10 course program includes all courses in the Spectral eLearning program, and provides a full end-to-end package on all of the basics of spectral interpretation.