features

Background

During the summer of 2014 I began investigating image stitching techniques and technologies for a NOAA sponsored UAS marine survey project.  In the summer of 2015 I was hired by the University of Minnesota Department of Aerospace Engineering and Mechanics to work on a Precision Agriculture project that also involves UAS’s and aerial image stitching.

Over the past few months I have developed a functional open-source image stitching pipeline written in python and opencv.  It is my intention with this series of blog postings to introduce this work and further explain our approach to aerial image processing and stitching.

Any software development project is a journey of discovery and education, so I would love to hear your thoughts, feedback, and questions in the comments area of any of these posts.  The python code described here will be released under the MIT open-source software license (one of my to-do list items is to publish this project code, so that will happen “soon.”)
feature-matching

 Why?

The world already has several high quality commercial image stitching tools as well as several cloud based systems that are free to use.  Why develop yet another image stitching pipeline?  There are several reasons we began putting this software tool chain together.

Outline

I will be posting new tutorials in this series as they are written.  Here is a quick look ahead at what topics I plan to cover:

Throughout the image collection and image stitching process there is art, science, engineering, math, software, hardware, aircraft, skill, and a maybe bit of luck once in a while (!) that all come together in order to produce a successful aerial imaging result.

Software Download

The software referenced in this tutorial series is licensed with the MIT license and available on the University of Minnestoa UAV Lab public github page under the ImageAnalysis repository.

Credits

2015-10-20 13:33:27 -0500 - Written by curt