Photogrammetry</p>
<h1>Image enhancement, filtering and sharpening</h1>
+<img src="jotunheimen-ir-2band.jpeg">
+<img src="jotunheimen-truecolor.jpeg">
+<img src="jotunheimen-std-ir-eq.jpeg">
<p>By Petter Reinholdtsen and Shanette Dallyn, 2005-05-01.</p>
-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/
+<p>This exercuse was performed by logging into jern.uio.no using ssh
+and runnign 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.
+<p>We tried to use svalbard/tm87.img, but it only have 5 bands. We
+decided to switch, and next tried jotunheimen/tm.img, which had 7
+bands.
-The pixel values in a given band is only a using a given range of
+<p>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.
+<p>The peak values of the histograms represent the the spectral
+sensitivity values that occure the most often with in the image band
+being analysed.
+
<h2>Evaluation of the different bands</h2>
-<h3>band 1, blue (0.45-0.52 um)</h3>
+<h3>band 1, blue (0.45-0.52 micrometer - um)</h3>
Visible light, and will display a broad range of values both over
land and water. Reflected from ice, as those are visible white and
peaks at 0 and 255. The mean value is 34.3403.
<h3>band 4, near-infraread (0.76-0.90 um)</h3>
+<img align="right" width="20%" src="jotunheimen-band4-hist.jpeg">
Water acts as an absorbing body so in the near infrared spectrum,
water features will appear dark or black meaning that all near
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
------------------
+<h3>Image enhancement</h3>
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
-------------------------
+<h3>Displaying colour images</h3>
+
+<p><img width="40%" src="http://home.online.no/~oe-aase/jotunheimen/jotun2000topper.jpg">
+<!-- img src="jotunheimen-map.jpeg" -->
+
+<img width="40%" src="jotunheimen-std-ir.jpeg">
-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:
+<p>Comparing a map we found on the web, and the standard infrared
+image composition, we can identify some features from the colors
+used:</p>
- - 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>
+<p><img align="right" width="40%"src="jotunheimen-therm-unsharp5x5.jpeg">
This one gave more lines showing the borders between the thermal
pixels.
projects / homepage.git / blobdiff