Book Image

Learning Geospatial Analysis with Python - Third Edition

By : Joel Lawhead
Book Image

Learning Geospatial Analysis with Python - Third Edition

By: Joel Lawhead

Overview of this book

Geospatial analysis is used in almost every domain you can think of, including defense, farming, and even medicine. With this systematic guide, you'll get started with geographic information system (GIS) and remote sensing analysis using the latest features in Python. This book will take you through GIS techniques, geodatabases, geospatial raster data, and much more using the latest built-in tools and libraries in Python 3.7. You'll learn everything you need to know about using software packages or APIs and generic algorithms that can be used for different situations. Furthermore, you'll learn how to apply simple Python GIS geospatial processes to a variety of problems, and work with remote sensing data. By the end of the book, you'll be able to build a generic corporate system, which can be implemented in any organization to manage customer support requests and field support personnel.
Table of Contents (15 chapters)
Free Chapter
1
Section 1: The History and the Present of the Industry
5
Section 2: Geospatial Analysis Concepts
10
Section 3: Practical Geospatial Processing Techniques

Performing a histogram stretch

A histogram stretch operation does exactly what its name says. It redistributes the pixel values across the whole scale. By doing so, we have more values at the higher-intensity level and the image becomes brighter. So, in this example, we'll reuse our histogram function, but we'll add another function called stretch() that takes an image array, creates the histogram, and then spreads out the range of values for each band. We'll run these functions on swap.tif and save the result in an image called stretched.tif:

import gdal_array
import operator
from functools import reduce

def histogram(a, bins=list(range(0, 256))):
fa = a.flat
n = gdal_array.numpy.searchsorted(gdal_array.numpy.sort(fa), bins)
n = gdal_array.numpy.concatenate([n, [len(fa)]])
hist = n[1:]-n[:-1]
return hist

def stretch(a):
"""
Performs a histogram stretch...