**数学与系统科学研究院**

**计算数学所学术报告**

**报告人**：
Yanfei Wang

Institute of Romote Sensing Applications, CAS

**报告题目**
Land Surface Parameter Retrieval via Kernel-driven BRDF Model &
Other Related Inversion Problems in Remote Sensing

**Abstract:**
The essence of the quantitative remote sensing is the inversion. There are
many different kinds of inverse problems in remote sensing. In this talk,
we focus on the land surface parameter retrieval by solving the
kernel-based BRDF model. Generally speaking, the description of a solvable
physical process should be overdetermined as described in Proposition 3 of
[Verstraete et al., 1996]. However such a requirement is hardly satisfied
even in the coming EOS era. Therefore, in order to solve the BRDF inversion
problem, Li et al (1998, 2001) utilizes a priori knowledge to convert the
problem into a over-determined system to find its least squares
error solution. So, from the computational view, both of them
actually solve an overdetermined system. In this talk, we investigate
the robust estimation of the land surface albedos by direct
solution of the kernel-driven BRDF model. Our method is based on
the deeply investigation of the spectrum of the linear driven
kernel, then we develop a direct method, i.e., a numerically
truncated singular value decomposition method, which can alleviate
the difficulties in numerical computation when the discrete kernel
is badly conditioned. This method can always find a set of suitable BRDF
coefficients even for poor sampled data. Numerical
performance is given for the widely used 18 data sets among the 73
data sets [Li et al., 2001].

We also present some other inversion problems in remote sensing, which is
still under study.

**报告时间**：2004年10月26日 下午3：00-4：00

**报告地点**：科技综合楼三层报告厅