Photogrammetry</p>
<h1>Image enhancement, filtering and sharpening</h1>
-<img width="40%" src="jotunheimen-std-ir-eq.jpeg">
<p>By Petter Reinholdtsen and Shanette Dallyn, 2005-05-01.</p>
decided to switch, and next tried jotunheimen/tm.img, which had 7
bands.
+<h2>Some notes on the digital images</h2>
+
<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.
image composition, we can identify some features from the colors
used:</p>
+<img align="right" width="40%" src="jotunheimen-ir-2band.jpeg">
<ul>
<li>water is black or green
</ul>
-<img align="right" width="40%" src="jotunheimen-ir-2band.jpeg">
<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
</ul>
+
+<p>We also tried to do histogram equilization on the standard infrared
+composition. This changed the colours in the image, making the
+previously green areas red, and the brown areas more light blue. In
+this new image, we can clearly see the difference between two kind of
+water, one black and one green. We suspect the green water might be
+deeper, but do not know for sure.</p>
+<img align="right" width="40%" src="jotunheimen-std-ir-eq.jpeg">
+
<h2>Filtering and image sharpening</h2>
<p>We decided to work on the grey scale version of the thermal infrared.
<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 tried a gradient filter using this 3x3 matrix. The matrix was
+chosen to make sure the sum of all the weights were zero, and to make
+sure the sum of horizontal, vertical and diagonal numbers were zero
+too.
+<p><table align="center">
+ <tr><td>1</td><td>2</td><td>-1</td></tr>
+ <tr><td>2</td><td>0</td><td>-2</td></tr>
+ <tr><td>1</td><td>-2</td><td>-1</td></tr>
+</table></p>
+
+<p>It gave a similar result to the edge detection.
-<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.
-<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.
+<p>We also tried unsharp filtering using this 3x3 matrix, selected
+also to make sure the sum of all the weights were zero, and making
+sure the high frequency changes had extra weight.
<p><table align="center">
- <tr><td>
+ <tr><td>-1</td><td>-1</td><td>-1</td></tr>
+ <tr><td>-1</td><td>8</td><td>-1</td></tr>
+ <tr><td>-1</td><td>-1</td><td>-1</td></tr>
+</table></p>
+
+<p>This gave similar results to the edge detection too.
+
+<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 (120 m, while the pixel size was
+30m). Because of this, we tried with a 5x5 matrix, making sure it
+sums up to 0.
+
+ <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>-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>
</table></p>
<p>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
+pixels. See the included image.
<H2>References</h2>
projects / homepage.git / blobdiff