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Poster Session R
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Image processing of airborne shortwave infrared split-spectral scanning data in detecting oil pools by using Remote Sensing techniques
3. Image geometrical deformation and rectification
Flight posture change was an important one of the factors that made image distort. Usually GCP transformation was carried out to solve this problem tieh topographic map. There were few stable and obvious geography marks or targets in this area, meantime ASISS image ground resolution was extraordinary low (only 30M * 30M), so few ground control points (GCP) which related to image were determined, and we couldn’t rectify the image with topographic map.
Aerial photomap which was selected as a image related to the ASISS image named slave image was geometrically corrected and mosaicked by optical processing. From the master and slave image, 25 GCP’s were selected and GCP file was established, and then image geometrical deformation was corrected by resembling with bi-linear interpolation method.
Extraction of Anomalous information and Analysis of Anomalous Results.
Shortwave infrared Remote Sensing data had a high correlativity nature because the high overlap of the scanner channels. The correlation coefficients between each two bands were almost over 0.80; Especially between band 3 and 4, the coefficient was high up to 0.98256; and the lowest one was 0.678998. The correlation weakened furtherly the anomalous information which was mainly included in band 6 and 7. So on the image by composing band 6, 7, 7, (R, G, B) the oil-pool Remote Sensing image anomalies were almost not displayed.
FACTOR analysis of R type model can remove the correlation, make the anomalous information outstanding, and reach the aim of recogonation and extraction.
Let X =[x1, x2, ……Xn]T as n-dimensional vectors that come from n bands, Y = [y1, y2, … …yn]T as n-dimensional vectors that come from n components and re decided by:
Y = A*X The A is a load factor matrix of n*n, and come from orthogonal transformation of relative coefficient matrix R = {rij} n*n according to eh biggest variance principle.
A = {aij] n*n, aij is load factor value of the band j to component, i, that is contribution of variance, meantime, it represents the amount of information of band j in component i. So the load factor matrix A
become the important basis of selecting component variate and extracting the anomalous information.
The image was transformed by FACTOR model and the images of 8 components were acquired individually. The load factor matrix us illustrated in Tab. 2. The selection of components and extraction of anomalous information was according to the spectral features of anomalous index and the numerical value of load factor.
From Tab.2 the load factor of the original bands in components was analyzed. It was found thath band 6 and 7 contribute to components 3 and 4 greatly and to the others small. This illustrated that the component 3 and 4 included most of anomalous information that was in the orgional badn 6 and 7.
Oil anomalous image (Image – 3) was gotten by selecting components 3 and 4 to make the colour composite 2,4,4 (R,G,B). The low absorption of hydrocarbon content nomalies in soil in characteristic bands showed dark black in the anomalous image. in the other words, eh dark color reflected the materials that behaved low absorption in the bands of 2.30 and 2.33mm. And from the spectral analysis of materials (vegetation, carbonate minerals, day and hydrocarbon) in test site only hydrocarbon materials have these spectral features. Comparing and integrated analyzing oil Remote Sensing anomalies image and the distribution of the known oil pool (Fig. 1), it was certified that the pattern of oil anomalies in the image was coincided with the distribution of the known oil pool.
Conclusion
Hydrocarbon content anomalies in soil are an important index for detecting oil resources by using Remote Sensing techniques. The low reflection by hydrocarbons at the wavelength of 2.31 and 2.35mm is the basis for extracting oil Remote Sensing anomalies. The shortwave infrared split-spectral scanner that included these two bands of 2.30, 2.33mm provided the technical promise. The image preprocessing and extraction and recognition of hydrocarbon content anomalies made direct detecting oil reservoirs become reality.
References
Zhu Zhenhai etc. Integrated eveluation fo Remote Sensing to oil-gas exploration, chinese science and technology press, 1991.
Table 1 : The work band and detecting target of the SISS
| Channel Number |
Con. Wb (nm) |
B. Wd (nm) |
Detecting Targets |
| 1 |
2087 |
50 |
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| 2 |
1600 |
100 |
Reflection features of various materials |
| 3 |
2143 |
100 |
Reflection features of Fe3+:1 |
| 4 |
2200 |
100 |
I = Absorption features of clay minerals |
| 5 |
2250 |
50 |
I |
| 6 |
2300 |
50 |
1:2 = Absorption features of carbonate |
| 7 |
2330 |
50 |
1:2:3 = Absorption features of hydrocarbon |
| 8 |
2450 |
100 |
2 |
Table 2: The load factor matric by R-type FACTOR transformation
| |
Band1 |
Band2 |
Badn3 |
Band4 |
Band5 |
Band6 |
Band7 |
Band8 |
| Comp.1 |
0.3571 |
0.3188 |
0.3666 |
0.3662 |
0.3698 |
0.3224 |
0.3570 |
0.3664 |
| Comp.2 |
-0.069 |
0.8653 |
-0.077 |
-0.149 |
-0.089 |
-0.478 |
-0.116 |
-0.1191 |
| Comp.3 |
-0.233 |
0.3095 |
-0.262 |
-0.262 |
-0.176 |
0.8234 |
-0.006 |
-0.059 |
| Comp.4 |
-0.223 |
0.3044 |
-0.216 |
0.0147 |
-0.077 |
-0.167 |
0.8961 |
-0.256 |
| Comp.5 |
-0.861 |
0.0098 |
0.3327 |
0.2961 |
0.2112 |
0.0025 |
-0.097 |
0.0806 |
| Comp.6 |
-0.146 |
-0.230 |
-0.124 |
-0.381 |
-0.100 |
-0.075 |
0.1684 |
0.8493 |
| Comp.7 |
-0.037 |
0.011 |
-0.504 |
-0.154 |
0.8436 |
-0.039 |
-0.074 |
-0.041 |
| Comp.8 |
-0.007 |
0.0005 |
-0.603 |
0.7197 |
-0.229 |
-0.0022 |
-0.113 |
0.2298 |
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