Remote sensing for resources development and environmental management: Remote sensing for resources development and environmental management

Damen, M. C. J.

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Multivolume work

Persistent identifier:: 856342815

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856342815

Language:: English

Additional Notes:: Volume 1-3 erschienen von 1986-1988

Editor:: Damen, M. C. J.

Document type:: Multivolume work

Volume

Persistent identifier:: 856343064

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Scope:: XV, 547 Seiten

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856343064

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(26,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Damen, M. C. J.

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: 2 Microwave data. Chairman: N. Lannelongue, Liaison: L. Krul

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Structure type:: Chapter

Chapter

Title:: Digital elevation modeling with stereo SIR-B image data. R. Simard, F. Plourde & T. Toutin

Document type:: Multivolume work

Structure type:: Chapter

163 At = i 1 [arc sin C - y - W - 6Wt ] 6W o i Next with the exact value of viewing time t^ for the point M^, it is possible to calculate the osculatory parameters (p,R,I,W,e x ,e y ) and the shuttle coordinates X and S'. Referring again to Figure 1, we can then write the following vector equation in a non-inertial (terrestrial) reference system: SMi = OMi - OS which is equivalent to the vector slant range that one can rewrite in terms of elements of Figure 3 as follows: r= xi - fx(t ± ) - tiVe] Normalizing the vector s’, one obtains s = IIsit *u which gives the direction cosine of the line SM^. One can obtain a more accurate value of the scalar slant range directly from the image data, so that only the orientation u of the vector slant range need be kept from 3. By combining these two parts, image scalar(s) and vector(H), a refined value is obtained for Is: Snew = s TT Referring to Figure 1, note that the local incidence angle 0^ is the angle between the two lines M^S and OM-^. The coordinates for the three points S, Mj[ and 0 (intersection of the first normal and the Z axis) are known and allow one to compute the direction cosine (l,m,n) of each line. Thus, the local incidence angle 0 L is given by: cos©l = 1}I2 + m^n^ + n i n 2 The advantage to use the image slant range module in the calculation of the new shuttle position at time t^ has been shown when comparing both slant range values as obtained from image and from orbit calculations. In fact, such a comparison has shown an average difference of 613 metres (based on a twenty point dataset). A second calculation on the same dataset but with a second initial shuttle position (modified with only one checkpoint for image slant range value), has shown the average difference reduced to approximately 50 metres. This second approach has been finally preferred due to the consistency of both measures of slant range. 2.2 Image resampling and filtering The rectification of raw SIR-B images into the intermediate level was achieved by using a polynomial fit between GCP locations obtained from raw image (but with a modified pixel location which excludes terrain parallax effect from the model) and those extracted from topographic maps. Using respectively 14 and 13 GCPs for the 29 and 53 degree SIR-B dataset, a quadratic polynomial regression was found to best fit the transformation with few independent check points (see Table 1). The residuals obtained for each image and for both sets of GCPs (test and model points) were always less than 37.5 metres (1.5 pixel) in either line or pixel direction. These results are in agreement with those published by Welch and Ehlers in 1985. Table 1. Quadratic polynomial fit residuals for SIR-B image rectification at the intermediate level. Incidence Residuals On Test On All Points Angle RMS (Pixel Points (Model & Test) Unit): (Total) (Total) 29° Pixel 1.49(5) 1.44(19) Line 1.28 1.22 53° Pixel 1.30(3) 1.28(16) Line 1.44 1.43 The SIR-B images were then resampled into a rotated UTM intermediate grid defined also at 25 metre pixel size. A 50 degree UTM rotation was applied in order to minimize the differences in geometry between the raw and the intermediate level products. Figure 4 shows both resampled images which cover an area 56.75 km long by 15.125 km. These images have also been radiometrically corrected by applying a 3 by 3 pixel window filter (Lee, 1981) in order to reduce the speckle effect (which was found to be necessary at the digital stereo matching level). Figure 4. SIR-B images (29° left, 53° right) processed at intermediate level.

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