+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.By Petter Reinholdtsen and Shanette Dallyn.
+By Petter Reinholdtsen and Shanette Dallyn, 2005-05-01.
-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/ +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/
-Tried to use svalbard/tm87.img, but it only have 5 bands. Next tried -jotunheimen/tm.img, which had 7 bands. +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 +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
@@ -82,139 +95,169 @@ over the entire range of possible values.
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
------------------
-
-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
-------------------------
-
-Comparing a map we found on the web,
-
+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.
-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
+
+
- - vegetation is light green and yellow.
+
+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.
-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 can get best contrast stretch by using the histogram
+equalisation. This gave us the widest range of visible separation
+between features.
-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
-8 -1 / -1 -1 -1 ]. This gave similar results to the edge detection
-too.
+
Comparing a map we found on the web, and the standard infrared +image composition, we can identify some features from the colors +used:
- -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 +
+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:
-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. +
'
+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.
+ +
'
+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.
+ +
'
+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.
+ +
'
+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)
+
+
'
+
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.
+ +| 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.
+ +
'
+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.
+ +| -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.
+ +| -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.
+ +