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Principle of High-resolution Airborne SAR Aerial triangulation supported by sparse GCPs
Lei Pang1,3, Jixian Zhang1, Mingbo Zhang3
1 Chinese Academy of Surveying and Mapping
Tel: 86 10 88229545, Fax: 0086 10 68211420
Email: Panglei.mail@163.com,
stecsm@public.bta.net.cn
Address: Beitaiping road 16, Haidian district, Beijing, P. R. China 100039
2Shandong University of Science and Technology
Phone: +86 538 6226813(0), Fax :( 0538) 6226163
Address: Institute of Geosciences, Shandong University of Science and Technology, Taian, Shandong, P. R. China, 271019
3Institute of Geographical Sciences and Natural Resources Research, CAS
Phone: +86 10 64861632-184, Fax :0086 10 64851844
Email: zhangmb@lreis.ac.cn
Address: Datun Road 3, Chaoyang district, Beijing, P. R. China 100101
1 Introduction
Presently, the Western Areas Mapping Mission (WAMM) is executing to map the relief in cloudy or soupy mountainous western area in China, where the high-resolution airborne SAR imagery has often been chosen as the irreplaceable remote sensing data resource. At the same time, the mission that how to acquire enough accuracy and quantity of ground control points (GCPs), which are difficult to be measured practically, has become an urgent demand for mapping tasks.
Although, the study to decrease the demand of GCPs in mosaicing RADARSAT or ERS SAR images or densing GCPs network has been done by Thierry Toutin(2003), Léonard Denise (1998) and Youssef Belgued (1998), there has no corresponding study on the method or models of high-resolution airborne SAR images triangulation.
Comparing with satellite-borne SAR images triangulation, the essence of high-resolution airborne SAR triangulation is still the 3D space positioning from SAR images (Chang Benyi, 2002) supported by only sparse GCPs or without GCPs. But, the method and mathematic models must be based on the characteristics of airborne SAR imaging conditions and flight tracks. So, we analyses the airborne SAR triangulation conditions, put forward the
corresponding mathematic models and introduce the high-resolution airborne SAR triangulation test in China in the following parts.
2 Airborne SAR Triangulation Conditions
We all know that InSAR technique can extract the 3D terrain information, but, without GCPs, the absolute space positions of the ground objects are still unknown to us. So, different from InSAR technique, we studied the high-resolution airborne SAR images triangulation conditions, which can be summarized as the follows:
- Airborne SAR images stereo-pairs based triangulation models;
- 60% overlap degree between the adjacent flight paths;
- Independent model block adjustment to compute target 3D coordinates only supported by sparse GCPs available;
- GPS/INS data supported airborne SAR triangulation without GCPs;
3 Mathematic Models
3.1 Independent model unit configuration

Fig.1 The independent model unit for airborne SAR imagery triangulation
To construct the independent model unit, we must pre-prepare the airborne SAR imagery. Firstly, we divide the imagery paths into many segments with some length, for example, the length of one synthetic aperture, and 20%~30% overlapping with adjacent segments in the same path. Then, take the flight direction as the X-axis, and range direction as Y. And, according to our flight course design, there is 60 percent overlap area between the two segments of independent model unit from adjacent imagery paths, so, the segments of the same overlap area in adjacent paths will construct the independent model unit for airborne SAR imagery triangulation.
3.2 Airborne SAR triangulation model based on independent models
Under the condition of only sparse GCPs available around the mapping area, the vector model of independent models based airborne SAR triangulation see as Fig.2. To make the processing simple, we adopt the stereo-pairs from the adjacent flight paths to buildup the independent model unit. And, each object P in the independent model unit will have the unique relative model coordinates, which can be transform into the absolute ground coordinates (X', Y', Z') through sequent space transform matrix. A more detailed explanation is as follows.
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