REMOTE SENSING CLASSIFICATION METHOD OF WETLAND BASED ON AN IMPROVED SVM
- 1College of Surveying and Geo-Informatics, Tongji University,Shanghai 200092, China
- 2Research Center of Remote Sensing & Spatial Information Technology, Tongji University, Shanghai 200092, China
Keywords: Multi-source remote sensing image, Wetland, Classification model, Support vector machine, Mixed kernel function
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.