+This image show the "true colour" version, with the blue range
+assigned to the blue colour, green range to green colour and red range
+to red colour.Assigment 8 + in GEG2210 + - Data Collection - Land Surveying, Remote Sensing and Digital + Photogrammetry
+By Petter Reinholdtsen and Shanette Dallyn, 2005-05-01.
-Tried to use svalbard/tm87.img, but it only have 5 bands. Next tried -jotunheimen/tm.img, which had 7 bands. +This exercise was performed by logging into jern.uio.no using ssh +and running ERDAS Imagine. Started by using 'imagine' on the command +line. The images were loaded from /mn/geofag/gggruppe-data/geomatikk/
-The pixel values in a given band is only a using a given range of +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 values. This is because sensor data in a single image rarely extend -over the entire range of possible values. +over the entire range of possible values.
+ +The peak values of the histograms represent the the spectral +sensitivity values that occure the most often with in the image band +being analysed.
+ +
+This image show the "true colour" version, with the blue range
+assigned to the blue colour, green range to green colour and red range
+to red colour.
Water acts as an absorbing body so in the near infrared spectrum,
water features will appear dark or black meaning that all near
@@ -61,164 +74,190 @@ band 4, near-infraread (0.76-0.90 um)
show most values between 7 and 110. The mean is 40.1144. There are
two peaks at 7 and 40.
-band 5, mid-infrared (1.55-1.75 um)
------------------------------------
+
+When we look at the linear contrast functions, we can move the +slope and shift values increasing or decreasing the contrast of the +image. For example, in the linear contrasting we moved the slope value +from 1.00 to 3.00 to obtain a brighter appearing image, and then we +moved the shift from 0 to 10 to recieve a sharper image.
-Displaying colour images ------------------------- +
+
-Comparing a map we found on the web,
-
+
- - ice and glaciers are white, while snow is light green.
+
+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.
- - non-vegetation is brown or dull red when closer to snow and - glaciers. +We can get best contrast stretch by using the histogram
+equalisation. This gave us the widest range of visible separation
+between features.
-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
+
Comparing a map we found on the web, and the standard infrared +image composition, we can identify some features from the colors +used:
- - vegetation is light green and yellow. +
+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:
-Filtering and image sharpening -============================== -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. +
'
+We decided to work on the grey scale version of the +near infrared (band4). We changed the colour assignment to use this +band for all three colours, giving us a gray scale image.
-This one gave more lines showing the borders between the thermal -pixels. +
'
+We applied the 3x3 low pass filter on this image, and +this gave us almost the same image as the original. If you look +closely you can see that some white dots in the original disapper, and +some of the water edges seem to blur very slightly.
-From: shanette Dallyn
'
+We also tried the 3x3 high pass filter on the band4 +grey scale image. This gave a very noisy image. Edges of vallies and +ice are not well defined. The black waters are still obvious.
-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: +
'
+We also tried the 3x3 edge detection, and this gave us
+an image that makes it difficult to distinguish elevation features
+such as the valleys. Rather, edge detection allows us to study main
+features in an area like the lakes. (insert band4 edge 3 image)
- Specialty Definition: Convolution
-
- (From Wikipedia, the free Encyclopedia)
+
'
+
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.
-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 . +| 1 | 2 | -1 |
| 2 | 0 | -2 |
| 1 | -2 | -1 |
The gradient filter used gave us enhancement on lines in the +vertical, horizontal and diagonal directions. This is seen by the +white lines that outline certain areas of main features like the +rivers within the vallies and some of the lakes.
-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. +
'
+When we rework the matrix to equal negative one, we end up with a +lot of noise in the image that also seems to blurr the image. Using a +negative one matrix is not optimal if you are trying to obtain +sharpness.
-If and are two independent random variables with probability densities and , -respectively, then the probability density of the sum is given by the -convolution . +| -1 | -1 | -1 |
| -1 | 7 | -1 |
| -1 | -1 | -1 |
'
+We then tried with a 3x3 matrix were the sum of all +values equals 1, to enhance the high frequency parts of the image.
-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). +| -1 | -1 | -1 |
| -1 | 9 | -1 |
| -1 | -1 | -1 |
This gave us a sharper looking image compared to the +result of the negative 1 filter. This is not really obvious unless +one is comparing the two images carefully. In order to see more +differences the matrix sums would have to be more then plus/minus one.
-I hope this will help! +