10-10-2012, 05:18 PM
Water Quality Retrieval and Performance Analysis Using Landsat Thermatic Mapper Imagery Based on LS-SVM
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Abstract
Because of the limited number of monitoring
points on the ground, the accuracy of traditional monitoring
methods using remote sensing was lower. This paper
proposed to use the Least Squares Support Vector Machine
(LS-SVM) theory to improve the accuracy of water quality
retrieval, which is suitable for the small-sample fitting. The
Radial Basic Function (RBF) was chosen as the kernel
function of the retrieval model, and the grid searching and
k-cross validation were used to choose and optimize the
parameters. This paper made use of the LS-SVM model and
some traditional retrieval models to retrieve concentration
of suspended matter. Comparing the results of experiment,
it showed that the proposed method had good performance
and at the same time, the complexity is lower and the speed
of the modeling was rapid.
INTRODUCTION
The use of remote sensing for water quality monitoring
is an uncertain problem [1]. How to establish an
appropriate model for the retrieval of the water quality
parameters is a difficult problem, when it referred to
either different water regions or different water
characteristic.
There are three mainly conventional methods for water
quality monitoring which are empirical method, semiempirical
method and analysis method [3]. In fact, these
methods are based on the estimation of linear regression
model to complete the water quality retrieval. However,
because of the complex chemical reaction and mutual
influence of various pollutants in the water, the
monitoring of the eutrophication in lake water should be a
non-linear prediction problem. Thus, using linear
regression to estimate the water quality parameters could
not get the accurate retrieval results.
THE MODEL OF LS-SVM
In 1995, Vapnik developed a novel algorithm called
support vector machine (SVM), which is a learning
machine based on statistical learning theory. SVM
implements the principle of structural risk minimization
for seeking to minimize an upper bound of the
generalization error rather than minimize the training
error. SVM has been applied to solve regression problems
by the introduction an alternative loss function, which
referred to support vector regression (SVR).
The Implication of the Water Quality Retrieval
The ground survey and remote sensing data was fused
by this paper, LS-SVM model was built to complete the
retrieval of the concentration of suspended matter. The
ground survey data is the concentration of suspended
matter value, which is also the output of the model, was
measured in Tai Lake. After the atmospheric correction,
the remote sensing data was used by this model.
By analyzing the reflection spectra of the Tai Lake
water, the reflectance values in the vicinity of 580nm and
810nm of the concentration of suspended matter is more
sensitive than others [8]. TM1-TM4 are just within the
scope of this spectrum. The reflectivity of 12 monitoring
points and the corresponding ground survey data of the
concentration of suspended matter were listed in the
Table I. The content of the Table I is the data for training.
From the Table I, it can be seen that 1
r is the reflectivity
of the TM1, so are the others.
CONCLUSION
Referred to analysis of relationship between the
remote sensing data and the ground survey data of Tai
lake, this paper established the water quality monitoring
model based on LS-SVM theory and algorithm.
The concentration of the suspended matter in Tai lake
was monitored by the proposed method and other
traditional methods. The results indicated that, the
proposed modeling method was simple, the adjustments
of the parameters were convenient and the speed of the
learning was fast. The non-linear retrieve system, which
was established by the LS-SVM method, can give a high
precision, so it was able to satisfy the demand of the
water quality monitoring.