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Learning Geospatial Analysis with Python-Second Edition

You're reading from   Learning Geospatial Analysis with Python-Second Edition An effective guide to geographic information systems and remote sensing analysis using Python 3

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Product type Paperback
Published in Dec 2015
Publisher Packt
ISBN-13 9781783552429
Length 394 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 FREE CHAPTER 2. Geospatial Data 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 Modeling 9. Real-Time Data 10. Putting It All Together Index

Classifying images


Automated Remote Sensing (ARS)is rarely ever done in the visible spectrum. ARS is the processing of an image without any human input. The most commonly available wavelengths outside of the visible spectrum are infrared and near-infrared. The following scene is a thermal image (band 10) from a fairly recent Landsat 8 flyover of the U.S. Gulf Coast from New Orleans, Louisiana to Mobile, Alabama. The major natural features in the image are labeled so that you can orient yourself:

As every pixel in this image has a reflectance value, it is information. Python can see these values and pick out features in the same way that we intuitively do by grouping the related pixel values. We can colorize the pixels based on their relation to each other to simplify the image and view the related features. This technique is called classification.

Classification can range from fairly simple groupings based only on some value distribution algorithm derived from the histogram, to complex methods...

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