Displaying colour images
------------------------
-Comparing a map we found on the web,
-<URL:http://home.online.no/~oe-aase/jotunheimen/jotun2000topper.jpg.>
+<p>Comparing a map we found on the web,
+<img src="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
+<ul>
- - ice and glaciers are white, while snow is light green.
+ <li>water is black or green
- - vegetation is red.
+ <li>ice and glaciers are white, while snow is light green.
- - non-vegetation is brown or dull red when closer to snow and
+ <li>vegetation is red.
+
+ <li>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
+</ul>
+
+<p>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
+<ul>
+ <li>water is black
+
+ <li>ice and glaciers are light blue, while snow is dark blue.
- - ice and glaciers are light blue, while snow is dark blue.
+ <li>vegetation is light green and yellow.
- - vegetation is light green and yellow.
+ <li>non-vegetation is red or brown.
- - non-vegetation is red or brown.
+</ul>
<h2>Filtering and image sharpening</h2>
-We decided to work on the grey scale version of the thermal infrared.
+<p>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
+<p>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
+<p>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
+<p>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
+<p><table align="center">
+ <tr><td>
+<tr><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td></tr>
+<tr><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td></tr>
+<tr><td>-1</td><td>-1</td><td>24</td><td>-1</td><td>-1</td></tr>
+<tr><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td></tr>
+<tr><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td></tr>
+ </table></p>
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
+ <p><table align="center">
+ <tr><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td></tr>
+ <tr><td>-1</td><td>-2</td><td>-2</td><td>-2</td><td>-1</td></tr>
+ <tr><td>-1</td><td>-2</td><td>32</td><td>-2</td><td>-1</td></tr>
+ <tr><td>-1</td><td>-2</td><td>-2</td><td>-2</td><td>-1</td></tr>
+ <tr><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td></tr>
+ </table></p>
-This one gave more lines showing the borders between the thermal
+<p>This one gave more lines showing the borders between the thermal
pixels.
From: shanette Dallyn <shanette_dallyn@yahoo.ca>
projects / homepage.git / blobdiff