Tag Archives: data

Landsat launch

On Feb 11th 2013 the Landsat Data Continuity Mission launches Landsat 8!! This is a hugely important launch as it effectively means that there will be a Landsat archive of comparable imagery running from the 1970s up to the present day. A launch party kit is available here. Landsat 5 was a lifeline after Landsat 6 failed to become functional and Landsat 7 has not been without its hiccups. My fingers are crossed that LDCM/Landsat 8 gets up there safely, gets on-line and starts providing many more amazing images.



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Python file handling

A lot of Python tinkering today, mainly in terms of file naming, management and extracting the contents. First off, how to create sensible file names: Date, Time, Details, Extension. So the code for this looks as follows:

from time import localtime
datetime_stamp = '%4d-%02d-%02dT%02d-%02d-%02d' % localtime()[:6]
title = "TestLogFile"
ext = "log"
print "Unique filename: %s-%s.%s" % (datetime_stamp, title, ext)

Which gives the following type of output:

Unique filename: 2011-10-31T10-09-33-TestLogFile.log

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I have spent the last day or so looking at the interaction between PIL and numpy. PIL is useful for quickly loading and manipulating images (size, subsets etc) but if you really want to get to grips with image processing you need to understand numpy and the array structure. If you have come from Matlab then it can be a bit confusing as the syntax is “similar but different”. Anyway, here are some basic snippets of code:

import Image as I # Import PIL
import numpy as N # Import Numpy
import pylab as P # Import Matplotlib functions
a = I.open("/home/al/Documents/temp_image_proc/bird/image4.jpg", "r") # Open the image
b = N.asarray(a) # Convert to Numpy array
red = b[:,:,0] # Extract red band
P.imshow(red) # Display the image

So we import the Image functions from PIL, numpy and the matplotlib plotting functions in pylab. Then we open up an image (.jpg format) convert it to an array, subset out the red band and display it. The image is shown here:

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Fourier in 4D?

A quick note for reference. Timeseries image or grid data can be fourier decomposed to create a new image dataset of the timeseries. This is based on work by Rogers at Oxford University. The components extracted from a timeseries are the amount (mean), variability (annual amplitude) and timing (annual phase). Various classification methods can then be used on the data e.g discriminant analysis, decision trees etc.

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Cool sites

Some cool open development at http://publiclaboratory.org/home

And lots of Python for RS links at http://cosmicproject.org/links.html

This browser looks interesting http://www.lunascape.tv/Europe/enGB.aspx#e001