+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/ +line. The images were loaded from /mn/geofag/gggruppe-data/geomatikk/
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. +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. +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
@@ -97,128 +97,161 @@ to red colour.
+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.
+ +
+
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+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 can get best contrast stretch by using the histogram
+equalisation. This gave us the widest range of visible separation
+between features.
+
+
+
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+
Comparing a map we found on the web, and the standard infrared 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 -different features: +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.
-
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
-pixels. Edge detection gave us the positions of glaciers and water.
-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.
+
'
+
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 |
| 1 | 2 | -1 |
| 2 | 0 | -2 |
| 1 | -2 | -1 |
It gave a similar result to the edge detection. +
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.
- -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.
+
'
+
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 |
| -1 | 8 | -1 |
| -1 | -1 | -1 |
This gave similar results to the edge detection too.
+
'
+
We then tried with a 3x3 matrix were the sum of all +values equals 1, to enhance the high frequency parts of the image.
-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. - -
| -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 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 |
| -1 | -1 | -1 |
| -1 | 9 | -1 |
| -1 | -1 | -1 |
This one gave more lines showing the borders between the thermal -pixels. See the included image. +
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.
-