mypapers/drafts/geg2210/assignment-8.html

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   9     <p><a href="http://www.geo.uio.no/geogr/geomatikk/oppgaver/bildeforbedring_eng.html">Assigment 8</a>
  10       in <a href="http://www.uio.no/studier/emner/matnat/geofag/GEG2210/index-eng.html">GEG2210</a>
  11       - Data Collection - Land Surveying, Remote Sensing and Digital
  12         Photogrammetry</p>
  13
  14     <h1>Image enhancement, filtering and sharpening</h1>
  15 <img src="jotunheimen-ir-2band.jpeg">
  16 <img src="jotunheimen-truecolor.jpeg">
  17 <img src="jotunheimen-std-ir-eq.jpeg">
  18
  19     <p>By Petter Reinholdtsen and Shanette Dallyn, 2005-05-01.</p>
  20
  21 <p>This exercuse was performed by logging into jern.uio.no using ssh
  22 and runnign ERDAS Imagine.  Started by using 'imagine' on the command
  23 line.  The images were loaded from /mn/geofag/gggruppe-data/geomatikk/
  24
  25 <p>We tried to use svalbard/tm87.img, but it only have 5 bands.  We
  26 decided to switch, and next tried jotunheimen/tm.img, which had 7
  27 bands.
  28
  29 <p>The pixel values in a given band is only a using a given range of
  30 values.  This is because sensor data in a single image rarely extend
  31 over the entire range of possible values.
  32
  33 <p>The peak values of the histograms represent the the spectral
  34 sensitivity values that occure the most often with in the image band
  35 being analysed.
  36
  37 <h2>Evaluation of the different bands</h2>
  38
  39 <h3>band 1, blue (0.45-0.52 micrometer - um)</h3>
  40
  41   Visible light, and will display a broad range of values both over
  42   land and water.  Reflected from ice, as those are visible white and
  43   reflect all visible light waves.  Histogram show most values between
  44   30 and 136.  Mean values of 66.0668.  There are one wide peak with
  45   center around 50.  There are two peaks at 0 and 255.
  46
  47 <h3>band 2, green (0.52-0.60 um)</h3>
  48
  49   Visible light, and will display a broad range of values both over
  50   land and water.  Reflected from ice, as those are visible white and
  51   reflect all visible light waves.  Histogram show most values from 8
  52   to 120.  The mean value is 30.9774.  There are two main peaks at 20
  53   and 27.  There is also a pie at 0.
  54
  55 <h3>band 3, red (0.60-0.69 um)</h3>
  56
  57   Visible light, and will display a broad range of values both over
  58   land and water.  Reflected from ice, as those are visible white and
  59   reflect all visible light waves.  Histogram show most values from 33
  60   t 135, with one wide peak around 52.  There are also seem to be two
  61   peaks at 0 and 255.  The mean value is 34.3403.
  62
  63 <h3>band 4, near-infraread (0.76-0.90 um)</h3>
  64 <img align="right" width="20%" src="jotunheimen-band4-hist.jpeg">
  65
  66   Water acts as an absorbing body so in the near infrared spectrum,
  67   water features will appear dark or black meaning that all near
  68   infrared bands are absorbed. On the other hand, land features
  69   including ice, act as reflector bodies in this band.  The histogram
  70   show most values between 7 and 110.  The mean is 40.1144.  There are
  71   two peaks at 7 and 40.
  72
  73 <h3>band 5, mid-infrared (1.55-1.75 um)</h3>
  74
  75   The ice, glaciers and water do not reflect any mid-infrared light.
  76   The histogram show most values between 1 and 178.  The mean is
  77   49.8098 and there are two peaks at 6 and 78, in addition to two
  78   peaks at 0 and 255.
  79
  80 <h3>band 6, thermal infrared (10.4-12.5 um)</h3>
  81
  82   Display the temperature on earth.  We can for example see that the
  83   ice is colder than the surrounding areas.  The histogram show most
  84   values between 36 to 122.  The mean is 102.734.  There are one wide
  85   peak around 53, in addition to two peaks at 0 and 255.
  86
  87 <h3>band 7, mid-infrared (2.08-2.35 um)</h3>
  88
  89   The ice, glaciers and water do not reflect any mid-infrared
  90   frequencies.  The histogram show most values between 77 and 150.
  91   The mean is 24.04, and there are one wide peak at 130 and a smaller
  92   peak at 83, in addition to one peak at 0.
  93
  94 <h3>Image enhancement</h3>
  95
  96 We can get a good contrast stretch by using the histogram
  97 equalisation.  This will give us the widest range of visible
  98 separation between features.
  99
 100 <h3>Displaying colour images</h3>
 101
 102 <p><img width="40%" src="http://home.online.no/~oe-aase/jotunheimen/jotun2000topper.jpg">
 103 <!-- img src="jotunheimen-map.jpeg" -->
 104
 105 <img width="40%" src="jotunheimen-std-ir.jpeg">
 106
 107 <p>Comparing a map we found on the web, and the standard infrared
 108 image composition, we can identify some features from the colors
 109 used:</p>
 110
 111 <ul>
 112
 113  <li>water is black or green
 114
 115  <li>ice and glaciers are white, while snow is light green.
 116
 117  <li>vegetation is red.
 118
 119  <li>non-vegetation is brown or dull red when closer to snow and
 120    glaciers.
 121
 122 </ul>
 123
 124 <p>Next, we tried to shift the frequencies displayed to use blue for the
 125 red band, green for the near ir band and red for the mid ir (1.55-1.75
 126 um).  With this composition, we get some changes in the colours of
 127 different features:
 128
 129 <ul>
 130  <li>water is black
 131
 132  <li>ice and glaciers are light blue, while snow is dark blue.
 133
 134  <li>vegetation is light green and yellow.
 135
 136  <li>non-vegetation is red or brown.
 137
 138 </ul>
 139
 140 <h2>Filtering and image sharpening</h2>
 141
 142 <p>We decided to work on the grey scale version of the thermal infrared.
 143 This one has lower resolution then the rest of the bands, with 120m
 144 spatial resolution while the others have 30m spatial resolution.
 145
 146 <p>The high pass filtering seem to enhance the borders between the
 147 pixels.  Edge detection gave us the positions of glaciers and water.
 148 We tried a gradient filter using this 3x3 matrix: [ 1 2 -1 / 2 0 -2 /
 149 1 -2 -1 ].  It gave a similar result to the edge detection.
 150
 151
 152 <p>We also tried unsharp filtering using this 3x3 matrix: [ -1 -1 -1 / -1
 153 8 -1 / -1 -1 -1 ].  This gave similar results to the edge detection
 154 too.
 155
 156 <p>We started to suspect that the reason the 3x3 filters gave almost the
 157 same result was that the fact that the spatial resolution of the
 158 thermal band is actually 4x4 pixels.  Because of this, we tried with a
 159 5x5 matrix, making sure it sums up to 0.
 160
 161 <p><table align="center">
 162   <tr><td>
 163     <tr><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td></tr>
 164     <tr><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td></tr>
 165     <tr><td>-1</td><td>-1</td><td>24</td><td>-1</td><td>-1</td></tr>
 166     <tr><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td></tr>
 167     <tr><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td></tr>
 168   </table></p>
 169
 170 Next, we tried some different weight:
 171
 172   <p><table align="center">
 173     <tr><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td></tr>
 174     <tr><td>-1</td><td>-2</td><td>-2</td><td>-2</td><td>-1</td></tr>
 175     <tr><td>-1</td><td>-2</td><td>32</td><td>-2</td><td>-1</td></tr>
 176     <tr><td>-1</td><td>-2</td><td>-2</td><td>-2</td><td>-1</td></tr>
 177     <tr><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td></tr>
 178   </table></p>
 179
 180 <p><img align="right" width="40%"src="jotunheimen-therm-unsharp5x5.jpeg">
 181 This one gave more lines showing the borders between the thermal
 182 pixels.
 183
 184 From: shanette Dallyn <shanette_dallyn@yahoo.ca>
 185 Subject: Re: My notes from todays exercise
 186 To: Petter Reinholdtsen <pere@hungry.com>
 187 Date: Sat, 30 Apr 2005 15:16:59 -0400 (EDT)
 188
 189 Hey Petter!
 190  Allright, I looked up some stuff on statistics and the most valuable
 191 conclusion that I can come up with for the histograpm peak question is:
 192 "The peak values of the histograms represent the the spectral sensitivity
 193 values that occure the most often with in the image band being analysed"
 194
 195 For the grey level question go to http://www.cs.uu.nl/wais/html/na-dir/sci/
 196 Satellite-Imagery-FAQ/part3.html I found this and thought that the first major
 197 paragraph pretty much answered the question for the grey levels.
 198
 199 theory of convolution:
 200
 201                       Specialty Definition: Convolution
 202
 203                    (From Wikipedia, the free Encyclopedia)
 204
 205 In mathematics and in particular, functional analysis, the convolution
 206 (German: Faltung) is a mathematical operator which takes two functions and and
 207 produces a third function that in a sense represents the amount of overlap
 208 between and a reversed and translated version of .
 209
 210 The convolution of and is written . It is defined as the integral of the
 211 product of the two functions after one is reversed and shifted.
 212
 213 The integration range depends on the domain on which the functions are
 214 defined. In case of a finite integration range, and are often considered as
 215 cyclically extended so that the term does not imply a range violation. Of
 216 course, extension with zeros is also possible.
 217
 218 If and are two independent random variables with probability densities and ,
 219 respectively, then the probability density of the sum is given by the
 220 convolution .
 221
 222 For discrete functions, one can use a discrete version of the convolution. It
 223 is then given by
 224
 225 When multiplying two polynomials, the coefficients of the product are given by
 226 the convolution of the original coefficient sequences, in this sense (using
 227 extension with zeros as mentioned above).
 228
 229 Generalizing the above cases, the convolution can be defined for any two
 230 square-integrable functions defined on a locally compact topological group. A
 231 different generalization is the convolution of distributions.
 232
 233 I hope this will help!
 234
 235 Shanette
 236
 237     <hr>
 238     <address><a href="mailto:pere@hungry.com">Petter Reinholdtsen</a></address>
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