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Poster Session
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Forest vegetation information of multispectral image
from space and it's false color display tradeoff
- A discussion on mixed image subset of false color composite for feature enhancement .
Three image subsets are mentioned above i.e. subset 1 ( TM1,TM2,Tm3…TM7) formed by the original six base data of landsat TM images (expect the thermal infrared band 6) made up by principal components of K-L transformation and subset 3 composed of 30 independent rationing images generated from the same TM image data . we call it can mixed subset which is made up of one or two subsets above Therefore according to the principle for us to from the new subsets how should we form the mixed subsets in order to extract or enhance certain physical landscape information we follow we follow a principle that is called physical landscape in formation weighting and complement each other adopting such a principle we can effectively enhance any features we are interested in.
We select Dawopu image window to discuss the effectiveness of mixed subset in feature enhancement comparing mixed subset image (K1,K2,T4) (color plate 5) with K-L transformation image (K1,K2,K3) (color plate 3) we can discover that the false color composite image formed by mixed subset has a higher ability in vegetation classification for example a cutover a kilometer north of Lujuanzi displayed as a unitary pattern in the standard false color image but the same area can be divided into three patterns of different colors in the image formed by (K1,K2, T4) it illustrates that the region can be further classified in vegetation for example according to 1:35000 color infrared airphotos, there is brush around Majiazi village a km south east to Lujuanzi but in the orthogonal transform image the pattern is divided in to yellow and light blue standing for buch and grass in the mixed subset image the same patter is cut into four separate parts by four color similarity the wide flood land near Shihu village in color plate 5 displays as a uniform yellow color but in color image of (T5,T4,K1) (color plate 6) it shows as four colored pattern which illustrate the difference of water condition of various land cover types if analyzed with great concentration variable can be obtained.
- An approach to external forest vegetation information from
multi temporal NOAA-AVHRR image.
Although it is obvious unrealistic for using above mentioned image processing methods to study global scale or continent sized forest vegetation it is very important to illustrate changes of global environment .People are forced to concern on finding a way to extract vegetation information from NOAA-AVHRR digital images .such vegetation cover types as force
Brush grassland or meadow have their own life rhythm which can be reflected by vegetation index vegetation index mentioned here is defined as the ratio of observed values of the second channel to the first channel of AVHRR the ratio is a function of time which values reflects of vegetation and strength of photosynthesis moreover it's deeply influenced by background (siol) as a matter of fact VI is the index of vegetation landscape.
The researched AVHRR image is a 512*512 pixel image window of Dalainor Inner Mongolia to the North West lie Great Xingan Mts to the south there stands mountain QiLaoTu with Silamuren river passes through and dalai nor lake in the center .Te time that the four images were received is the vegetation gouts season in 1989 and respectively the date is May 4 and June 8 July 2 and August 13 . The original images are strictly matched by mercator projection transform after computing the VI of four original images separately we co responsibility get four temporary VI images to separate the vegetation types from each other the four VI images are K-L transformed and the temporal dimension coordinate is completed. The result K-L transformation is shown on Table 5.
Table 5. eigenvalues and eigen vectors of K-L transform for vegetation index
| Temporal number |
1(5.04) |
2,(6,08 ) |
3(7,03) |
4(8,13) |
| Mean |
189.98 |
232.27 |
154.07 |
153.85 |
| Variance |
25.029 |
33.752 |
77.434 |
77.507 |
| Correlation matrix |
1 |
626.48 |
211.07 |
1415.0 |
1428.5 |
| 2 |
211.07 |
1139.2 |
1056.4 |
1059.4 |
| 3 |
1415.0 |
1 1056.4 |
5996.0 |
5825.6 |
| 4 |
1428.5 |
1059.4 |
5825.6 |
6007.3 |
principal component
| 1
|
2
|
3
|
4
|
| eigen values |
0.899 |
0.068 |
0.020 |
0.013 |
| |
1 |
-0.169 |
0.094 |
0.977 |
0.086 |
| 2 |
-0.133 |
-0.988 |
0.072 |
-0.007 |
| 3 |
-0.690 |
0.084 |
-0.189 |
-0.694 |
| 4 |
-0.691 |
0.083 |
-0.065 |
0.715 |
Table 5 shows that after the principal transformation the information of vegetation or vegetation index basically gather to the first principal component the first principal component image has greatly reveled the difference between various types of vegetation which can be effectively distinguished by the image segment technology in processing the pixels with the same characteristics get the incontinently the pixel on the region should be similar to each other and some variable characteristics of the pixels vary from one region to another therefore the edged can be made out.
The segment to some honogeneouse attribute PK of a two dimensional image point matrix X(1,J) is to divide X into some non subsets X11,X2,X3…,Xk. They meet the conditions follows:
Eq.
The method to give a threshold is used in image segment the value of thres hold operator Tk determined according to the histogram map :
Eq.
In order to draw the outline of the edges horizontal and vertical direction should be examed at the same time.
Eq.
The first principal image segment is completed by the new developed function SEGMT thresholds divide the image DN value region in to 9 parts the computer scans and searches one by one and in the meantime prepared color values are given by this way we successfully get color images according to threshold segment checked with the vegetation map of Chifen inner Mongolia the types of land cover which the various colors in the segment image stand for are shown on table 6.
Table 6. Image segmentation for the first principle component of multi-temporal vegetation index VI.
| D N |
0-32 |
32-64 |
64-96 |
96-128 |
128-160 |
160-192 |
192-224 |
224-254 |
254-255 |
| Color |
dark green | apple green | blue green |
pea green | light yellow | brown yellow |
dark yellow | red |
magenta |
| Vegetation cover-type | forest or shrub |
alpine grass land | meadow |
grass land | deyenerate grass land |
crop land | bare land |
Segment of single spectral image is equal to the classification of multi spectral image . Especially for forest and grass land region with background more unitary and objects largely continuous the segmentation result of the principal component image has obvious classification significances segment image are the authentic accords of the distribution of forest or brush in the section of great Xinggan Mts. and Mountain QiLaoTu.
References
- Nesdis, NASA,global vegetation index user's hand book, aug., 1983.
- Tucker,c.j.et al., African land-cover classification using satellite data ,
SCIENCE, vol, 227, No. 4685, 1985.
- MOik,J.G., Digital processing of remotely sensed images ( NASA SP; 431) 1980.
- Simonett, D.S., et al., Manual of remote sensing and ed. Virginia, vol. I ,1983.
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