Abstract
Abstract. The increase of population and economic development, especial the land use and urbanization bring the wetland resource a huge pressure and a serious consequence of a sharp drop in the recent years. Therefore wetland eco-environment degradation and sustainable development have become the focus of wetland research. Remote sensing technology has become an important means of environment dynamic monitoring. It has practical significance for wetland protection, restoration and sustainable utilization by using remote sensing technology to develop dynamic monitoring research of wetland spatial variation pattern. In view of the complexity of wetland information extraction performance of the SVM classifier, this paper proposes a feature weighted SVM classifier using mixed kernel function. In order to ensure the high-accuracy of the classification result, the feature spaces and the interpretation keys are constructed by the properties of different data. We use the GainRatio (featurei) to build the feature weighted parameter h and test the different kernel functions in SVM. Since the different kernel functions can influence fitting ability and prediction accuracy of SVM and the categories are more easily discriminated by the higher GainRatio, we introduce feature weighted ω calculated by GainRatio to the model. Accordingly we developed an improved model named "Feature weighted & Mixed kernel function SVM" based on a series of experiments. Taking the east beach of Chongming Island in Shanghai as case study, the improved model shows superiority of extensibility and stability in comparison with the classification results of the experiments applying the Minimum Distance classification, the Radial Basis Function of SVM classification and the Polynomial Kernel function of SVM classification with the use of Landsat TM data of 2009. This new model also avoids the weak correlation or uncorrelated characteristics' domination and integrates different information sources effectively to offer better mapping performance and more accurate result. The accuracy resulted from the improved model is better than others according to the Overall Accuracy, Kappa Coefficient, Omission Errors and Commission Errors.
Highlights
Wetlands are considered as one of the most biologically diverse ecosystems, serving as critical habitat and productive intertidal zones to a wide range of wild plant and animal
The wetland take on the characteristics of a distinct ecosystem for a land area saturated with water, either permanently or seasonally
Biodiversity loss occurs in wetland systems because of land use changes, habitat destruction, pollution, exploitation of resources, and invasive species
Summary
Wetlands are considered as one of the most biologically diverse ecosystems, serving as critical habitat and productive intertidal zones to a wide range of wild plant and animal. Remote sensing technology becomes the important means of earth observation for superiority in extensive regional coverage; continuous acquisition of data; accurate and up-to-date information; comparability of a large archive of historical data and so on. It has been widely used in resources investigation, classification, change detection, landscape pattern change analysis and function assessment of wetland over recent two decades. Wetlands information extraction using remote sensing data is primary step during wetland monitoring When it comes to support vector machine, no one can deny that SVM made great contribution to classification and regression analysis. The problems with SVMs are the high algorithmic complexity; selection of the kernel function parameters; extensive memory requirements of the required quadratic programming in largescale tasks which need to be solved properly
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