An introduction to satellite image analysis using Python + OpenCV and JavaScript + Google Earth Engine

Last update: Jan 03, 2023

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Overview

A Gentle Introduction to Satellite Image Processing

Welcome to this introductory course on Satellite Image Analysis!

Satellite imagery has become a primary data source in the natural sciences, economics, archaeology, sustainability, and many other domains which utilize geospatial intelligence.

Indeed, the wide variety of imagery sources and the vast amounts of data being collected are now challenging our ability to manage, process, and derive useful insight from this information.

Motivated by this, the primary objective of the course is to provide a systematic introduction to computer-based processing of satellite imagery techniques for enhancing, processing, and extracting spatial information from imagery.

This course emphasizes the practical application of computer-based image processing (for total beginners) using programming techniques capable of analyzing large quantities of imagery data.

The tools used include Python/OpenCV and JavaScript/Google Earth Engine.

Learning Outcomes

1. Understand practical computer programming techniques for processing satellite imagery.
2. Develop introductory Python-based approaches for object detection and extraction.
3. Utilize introductory JavaScript for running image processing tasks using cloud computing.

Syllabus

Acknowledgements

This repository is the codebase associated with the satellite image processing class supported and delivered by George Mason University (GGS416).

Contributors

Edward Oughton (eoughton [at] gmu.edu)
Mirza Waleed (mirzawaleed197 [at] gmail.com)
Bonface Osoro (bosoro [at] gmu.edu)

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Releases(v0.0.1)

v0.0.1(Jan 31, 2022)

This is the initial version of the repo and will undergo weekly development until early May 2022.
Source code(tar.gz)
Source code(zip)

An introduction to satellite image analysis using Python + OpenCV and JavaScript + Google Earth Engine

Related tags

Overview

A Gentle Introduction to Satellite Image Processing

Learning Outcomes

Syllabus

Acknowledgements

Contributors

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A series of convenience functions to make basic image processing operations such as translation, rotation, resizing, skeletonization, and displaying Matplotlib images easier with OpenCV and Python.

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Owner

Edward Oughton

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