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+<html>
+ <head>
+ <title></title>
+ </head>
+
+ <body>
+ <h1></h1>
+
+Here are my notes from today.
+
+
+Logged into jern.uio.no using ssh to run ERDAS Imagine. Started by
+using 'imagine' on the command line. The images were loaded from
+/mn/geofag/gggruppe-data/geomatikk/
+
+Tried to use svalbard/tm87.img, but it only have 5 bands. Next tried
+jotunheimen/tm.img, which had 7 bands.
+
+The pixel values in a given band is only a using a given range of
+values. This is because sensor data in a single image rarely extend
+over the entire range of possible values.
+
+Evaluation of the different bands
+=================================
+
+band 1, blue (0.45-0.52 um)
+---------------------------
+
+ Visible light, and will display a broad range of values both over
+ land and water. Reflected from ice, as those are visible white and
+ reflect all visible light waves. Histogram show most values between
+ 30 and 136. Mean values of 66.0668. There are one wide peak with
+ center around 50. There are two peaks at 0 and 255.
+
+band 2, green (0.52-0.60 um)
+----------------------------
+
+ Visible light, and will display a broad range of values both over
+ land and water. Reflected from ice, as those are visible white and
+ reflect all visible light waves. Histogram show most values from 8
+ to 120. The mean value is 30.9774. There are two main peaks at 20
+ and 27. There is also a pie at 0.
+
+band 3, red (0.60-0.69 um)
+--------------------------
+
+ Visible light, and will display a broad range of values both over
+ land and water. Reflected from ice, as those are visible white and
+ reflect all visible light waves. Histogram show most values from 33
+ t 135, with one wide peak around 52. There are also seem to be two
+ peaks at 0 and 255. The mean value is 34.3403.
+
+band 4, near-infraread (0.76-0.90 um)
+-------------------------------------
+
+ Water acts as an absorbing body so in the near infrared spectrum,
+ water features will appear dark or black meaning that all near
+ infrared bands are absorbed. On the other hand, land features
+ including ice, act as reflector bodies in this band. The histogram
+ show most values between 7 and 110. The mean is 40.1144. There are
+ two peaks at 7 and 40.
+
+band 5, mid-infrared (1.55-1.75 um)
+-----------------------------------
+
+ The ice, glaciers and water do not reflect any mid-infrared light.
+ The histogram show most values between 1 and 178. The mean is
+ 49.8098 and there are two peaks at 6 and 78, in addition to two
+ peaks at 0 and 255.
+
+band 6, thermal infrared (10.4-12.5 um)
+---------------------------------------
+
+ Display the temperature on earth. We can for example see that the
+ ice is colder than the surrounding areas. The histogram show most
+ values between 36 to 122. The mean is 102.734. There are one wide
+ peak around 53, in addition to two peaks at 0 and 255.
+
+band 7, mid-infrared (2.08-2.35 um)
+------------------------------------
+
+ The ice, glaciers and water do not reflect any mid-infrared
+ frequencies. The histogram show most values between 77 and 150.
+ The mean is 24.04, and there are one wide peak at 130 and a smaller
+ peak at 83, in addition to one peak at 0.
+
+Image enhancement
+-----------------
+
+We can get a good contrast stretch by using the histogram
+equalisation. This will give us the widest range of visible
+separation between features.
+
+Displaying colour images
+------------------------
+
+Comparing a map we found on the web,
+<URL:http://home.online.no/~oe-aase/jotunheimen/jotun2000topper.jpg.>
+and the standard infrared image composition, we can identify some
+features from the colors used:
+
+ - water is black or green
+
+ - ice and glaciers are white, while snow is light green.
+
+ - vegetation is red.
+
+ - non-vegetation is brown or dull red when closer to snow and
+ glaciers.
+
+Next, we tried to shift the frequencies displayed to use blue for the
+red band, green for the near ir band and red for the mid ir (1.55-1.75
+um). With this composition, we get some changes in the colours of
+different features:
+
+ - water is black
+
+ - ice and glaciers are light blue, while snow is dark blue.
+
+ - vegetation is light green and yellow.
+
+ - non-vegetation is red or brown.
+
+Filtering and image sharpening
+==============================
+
+We decided to work on the grey scale version of the thermal infrared.
+This one has lower resolution then the rest of the bands, with 120m
+spatial resolution while the others have 30m spatial resolution.
+
+The high pass filtering seem to enhance the borders between the
+pixels. Edge detection gave us the positions of glaciers and water.
+We tried a gradient filter using this 3x3 matrix: [ 1 2 -1 / 2 0 -2 /
+1 -2 -1 ]. It gave a similar result to the edge detection.
+
+
+We also tried unsharp filtering using this 3x3 matrix: [ -1 -1 -1 / -1
+8 -1 / -1 -1 -1 ]. This gave similar results to the edge detection
+too.
+
+We started to suspect that the reason the 3x3 filters gave almost the
+same result was that the fact that the spatial resolution of the
+thermal band is actually 4x4 pixels. Because of this, we tried with a
+5x5 matrix, making sure it sums up to 0.
+
+ -1 -1 -1 -1 -1
+ -1 -1 -1 -1 -1
+ -1 -1 24 -1 -1
+ -1 -1 -1 -1 -1
+ -1 -1 -1 -1 -1
+
+Next, we tried some different weight:
+
+ -1 -1 -1 -1 -1
+ -1 -2 -2 -2 -1
+ -1 -2 32 -2 -1
+ -1 -2 -2 -2 -1
+ -1 -1 -1 -1 -1
+
+This one gave more lines showing the borders between the thermal
+pixels.
+
+From: shanette Dallyn <shanette_dallyn@yahoo.ca>
+Subject: Re: My notes from todays exercise
+To: Petter Reinholdtsen <pere@hungry.com>
+Date: Sat, 30 Apr 2005 15:16:59 -0400 (EDT)
+
+Hey Petter!
+ Allright, I looked up some stuff on statistics and the most valuable
+conclusion that I can come up with for the histograpm peak question is:
+"The peak values of the histograms represent the the spectral sensitivity
+values that occure the most often with in the image band being analysed"
+
+For the grey level question go to http://www.cs.uu.nl/wais/html/na-dir/sci/
+Satellite-Imagery-FAQ/part3.html I found this and thought that the first major
+paragraph pretty much answered the question for the grey levels.
+
+theory of convolution:
+
+ Specialty Definition: Convolution
+
+ (From Wikipedia, the free Encyclopedia)
+
+In mathematics and in particular, functional analysis, the convolution
+(German: Faltung) is a mathematical operator which takes two functions and and
+produces a third function that in a sense represents the amount of overlap
+between and a reversed and translated version of .
+
+The convolution of and is written . It is defined as the integral of the
+product of the two functions after one is reversed and shifted.
+
+The integration range depends on the domain on which the functions are
+defined. In case of a finite integration range, and are often considered as
+cyclically extended so that the term does not imply a range violation. Of
+course, extension with zeros is also possible.
+
+If and are two independent random variables with probability densities and ,
+respectively, then the probability density of the sum is given by the
+convolution .
+
+For discrete functions, one can use a discrete version of the convolution. It
+is then given by
+
+When multiplying two polynomials, the coefficients of the product are given by
+the convolution of the original coefficient sequences, in this sense (using
+extension with zeros as mentioned above).
+
+Generalizing the above cases, the convolution can be defined for any two
+square-integrable functions defined on a locally compact topological group. A
+different generalization is the convolution of distributions.
+
+I hope this will help!
+
+Shanette
+
+ <hr>
+ <address><a href="mailto:pere@hungry.com">Petter Reinholdtsen</a></address>
+<!-- Created: Sun May 1 13:25:38 CEST 2005 -->
+<!-- hhmts start -->
+Last modified: Sun May 1 13:32:31 CEST 2005
+<!-- hhmts end -->
+ </body>
+</html>
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