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Learning Geospatial Analysis with Python

You're reading from   Learning Geospatial Analysis with Python If you know Python and would like to use it for Geospatial Analysis this book is exactly what you've been looking for. With an organized, user-friendly approach it covers all the bases to give you the necessary skills and know-how.

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Product type Paperback
Published in Oct 2013
Publisher Packt
ISBN-13 9781783281138
Length 364 pages
Edition 1st Edition
Languages
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Author (1):
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Joel Lawhead Joel Lawhead
Author Profile Icon Joel Lawhead
Joel Lawhead
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Table of Contents (12) Chapters Close

Preface 1. Learning Geospatial Analysis with Python 2. Geospatial Data FREE CHAPTER 3. The Geospatial Technology Landscape 4. Geospatial Python Toolbox 5. Python and Geographic Information Systems 6. Python and Remote Sensing 7. Python and Elevation Data 8. Advanced Geospatial Python Modelling 9. Real-Time Data 10. Putting It All Together Index

Extracting features from images


The ability to classify an image leads us to another remote-sensing capability. Now that you've worked with shapefiles over the last few chapters, have you ever wondered where they come from? Vector GIS data such as shapefiles are typically extracted from remotely-sensed images like the examples we've seen so far. Extraction normally involves an analyst clicking around each object in an image and drawing the feature to save it as data. But it is also possible with good remotely-sensed data and proper pre-processing to automatically extract features from an image.

For this example we'll take a subset of our Landsat 8 thermal image to isolate a group of barrier islands as seen in the following screenshot:

You can download this image here:

http://geospatialpython.googlecode.com/files/islands.zip

Our goal with this example is to automatically extract the three islands in the image as a shapefile. But before we can do that, we need to mask out any data we aren't...

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