This course was carefully developed around factors that need to be considered to properly assess the quality of your data. Learners will be able to discriminate between data levels, mitigate visual flaws, and appropriately correct flaws in data.

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This course explores the applications of spectral data across various industries, as well as detailing the interactions between the electromagnetic spectrum and its effects on the Earth’s surface. Learners will also be able to understand the various interactions between incident, reflected, and emitted energy. 

This course explores the relationship between values at the ground level, and values in your data. In addition, the course details the importance of appropriate image registration and creating accurate image mosaics to conduct spectral analysis.

This course details the various applications of image classification. By the end of this course, learners will understand the value of training data in image classification, and how to create training classes with, or without training data involved in image processing. In addition, learners will have an opportunity to create an annotated product utilizing variables derived from their classification matrix.

This course will teach the value of accuracy assessments when conducting image classification. By the end of this course, learners will be able to identify the variables that must be accurate to ensure their image classification meets the ground truth values.

This course serves as a deeper dive into the affects of the atmosphere when conducting remote sensing. By the end of this course, learners will understand the basics of image reflection, absorption, and transmission, as well as the underlying effects aerosols in the atmosphere have on data. In addition, learners will gain a fundamental understanding of how to detect, correct, and disseminate intelligence products that have been processed to eliminate unwanted effects in the atmosphere.

This course will guide learners to the various image data repositories that are accessible for spectral analysis. By the end of this course, learners will be able to identify which data repository is best suited for their analysis, as well as how to properly navigate, identify the data level needed, and successfully load the data in ENVI.

This course will introduce learners to the change detection workflow. By the end of this course, students will be able to conduct a proper change assessment of a scene utilizing various image comparison methods. In addition, learners will understand how to properly utilize a time series for spectral analysis.

This course navigates the importance of comparing and combining various data types together for the development of an accurate analytical product. Learners will understand the value in utilizing differing data sources, such as LiDAR and vector data in ENVI. This course will also demonstrate the integration of ENVI with the ArcGIS suite of tools.