X-Git-Url: http://pere.pagekite.me/gitweb/homepage.git/blobdiff_plain/c01fa63aeaf1a3ac6c8634c152d9d1b13c6de2b7..fbb1eda6f330944447ea70536857e33891b958a1:/mypapers/drafts/geg2210/assignment-8.html diff --git a/mypapers/drafts/geg2210/assignment-8.html b/mypapers/drafts/geg2210/assignment-8.html index 7f58d4eea9..1348096fe3 100644 --- a/mypapers/drafts/geg2210/assignment-8.html +++ b/mypapers/drafts/geg2210/assignment-8.html @@ -12,7 +12,6 @@ Photogrammetry

Image enhancement, filtering and sharpening

-

By Petter Reinholdtsen and Shanette Dallyn, 2005-05-01.

@@ -24,6 +23,8 @@ line. The images were loaded from /mn/geofag/gggruppe-data/geomatikk/ decided to switch, and next tried jotunheimen/tm.img, which had 7 bands. +

Some notes on the digital images

+

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. @@ -111,6 +112,7 @@ separation between features. image composition, we can identify some features from the colors used:

+ -

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 @@ -142,6 +143,15 @@ different features: + +

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.

+ +

Filtering and image sharpening

We decided to work on the grey scale version of the thermal infrared. @@ -150,21 +160,39 @@ 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 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. +

+ + + +
12-1
20-2
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. +

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.

- + + +
+
-1-1-1
-18-1
-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 (120 m, while the pixel size was +30m). Because of this, we tried with a 5x5 matrix, making sure it +sums up to 0. + +

@@ -184,60 +212,7 @@ Next, we tried some different weight:
-1-1-1-1-1
-1-1-1-1-1
-1-124-1-1

This one gave more lines showing the borders between the thermal -pixels. - -From: shanette Dallyn -Subject: Re: My notes from todays exercise -To: Petter Reinholdtsen -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.

References