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Mapping without the sun
Zhang, Jixian

Jianming Gong a,b 1 *, Xiaomei Yang b , Chenghu Zhou b , Xiaoyu Sun b , Cunjin Xue b
a School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei 430079, P. R. China
b State key Lab of Resources and Environment Information System, CAS, Beijing 100101, P. R. China
Commission VI, WG VIM
KEY WORDS: RFM, RPC, Beijing-1 Small Satellite, Orthorectification
With two pushbroom sensors of which one is 4m panchromatic sensor, and the other is a 32m multispectral sensor, Beijing-1 small
satellite have both the features of the high resolution of SPOT and the multi-spectrum of Landsat. High resolution satellite imageries
(HRSIs) increase the need for higher accuracy of data modeling. The satellite orbit, position, attitude angles and interior orientation
parameters have to be adjusted in the geometrical model to achieve optimal accuracy with the use of a minimum number of Ground
Control Points (GCPs). However, most high resolution satellite vendors do not intend to publish their sensor models and ephemeris
data. Therefore, a range of alternative and practical approaches should be developed for extracting accurate 2D and 3D terrain
information from HRSI. Aimed at the features of Beijing-1 small satellite high resolution panchromatic imagery, an
orthorectification method based on Rational Function Model (RFM) is proposed in the paper. RFM is a generalized sensor model to
perform orthorectification in no need of orbit parameters and sensor imaging parameters which is independent on sensors or
platforms and supports any object space coordinate system with a variable coordinate system. The improved RPM based on block
adjustment with compensation for exterior orientation biases is then refined and evaluated, as is the means to enhance the original
Rational Polynomial Coefficients (RPCs) through a bias correction procedure. Compared to linear transformation and polynomial
transform, RFM has the highest positioning accuracy, because RPC is determined by applying the least squares principle to GCP
data, approximate error can be evenly distributed through RFM rectification. An amount of experiments have proved that a sub-pixel
positioning accuracy can be achieved by using refined RFM to rectify the Beijing-1 small satellite image which is close to the
accuracy of the rigorous sensor model.
In order to ensure well and truly utility for Beijing-1 HRSI data
which has a high resolution of 4m, a higher rectification
accuracy of image processing has been taken into account
(Chen Zhengchao, 2006). Besides geometric accurate correction,
sensor model must be established to meet the needs of high
accuracy in orthorectification. There are two categories of
sensor models: physical sensor models and generalized sensor
models (C.S. Fraser, 2000; Okamoto Atsushi, 1999; Toutin T.,
2002). Establishment of physical sensor models requires the
information such as the physical structure of the sensor and the
imaging mode. However, for secrecy, information such as the
lens structure, imaging mode and orbit parameters of some high
performance sensor has not been made public. Therefore, the
users usually can not build the rigorous models of these sensors
and require generalized sensor models that are independent on
sensors with a simple form and quick processing. RFM, linear
equations, and polynomial transform are the typical examples
of the generalized sensor models. Many kinds of senor models
and their accuracy have been studied by researchers at home
and abroad. Yong Hu et al from GeoICT, NYU and C.V. Tao
from Canada have done important research (Yong Hu, 2004;
C.V. Tao, 2001a; C.V. Tao, 2001b). Shulong Zhu from Hong
Kong Polytechnic University (Zhu shulong, 2004), Zhian
Zhang from Taiwan Central University (Tee-Ann Teo, 2005),
Xuwen Qin, Guo Zhang from Wuhan University (Qin Xuwen,
2005), and Yongsheng Zhang from Information Engineering
University all have done research on generalized sensor models
and accuracy evaluation (Zhang Yongsheng, 2004; Liu Jun,
2002). Research achievements available indicate: the accuracy
of RFM is better than that of polynomial model, and in regions
with small change of the terrain, RFM can use fewer GCPs to
achieve ideal accuracy. The sensors of the Beijing-1 high
resolution satellite have the key features of a long focal length
and a narrow field of view. Especially, under the condition of
the focal length of the panchromatic sensor is 1.371m, and the
field of view is 1.9 degrees RFM becomes prominent. Hence,
the RPC generalized sensor model is proper for the Beijing-1
high resolution satellite. Since RFM has considered the
influence of the height change ignored by traditional 2D
transformations, it is more accurate than the polynomial model
and spline function model (C.S. Fraser, 2006). Meanwhile,
without knowing the information about the sensor imaging
process, users can use simple RFM to rectify the relation
between image space and object space accurately. In the
development of Beijing-1 small satellite data processing
software, using a refined RFM to perform orthorectification on
the Beijing-1 small satellite data has achieved satisfactory
results (J.M. Gong, 2007).
* Corresponding author: Jiangming Gong, E-mail: gongjm@lreis.ac.cn. This work was supported by a grant from the Major State
Basic Research Development Program of China (973 Program) (No.2006CB701305) and the National High Technology
Research and Development Program of China (863 Program) (2006AA12Z146; No. 2005AA133013 ) .