Incorporation of texture information in a SVM method for classifying salt cedar in western China

Abstract

Salt cedar is prominent plant species for wind-erosion resistance and saline–alkaline tolerance in arid desert and saline–alkali soil in China. Although some studies have been published for documenting salt cedar dynamics in its invasive habitat in the United States, the study of salt cedar in its native habitat, e.g. China, is relative scarce. Recently, the reduction of salt cedar is remarkable in western China that effective methods for monitoring salt cedar dynamics are essential for ecological environment preservation. As such, this study aims to develop a support vector machine (SVM) to better mapping salt cedar with high spatial resolution remote sensing image. Specifically, our objectives are twofold: (1) to examine the role that textures play in the SVM classification and further derive the optimal window size for extracting textures and (2) to determine optimal values for two vital parameters γ and C in the SVM method. To test the effectiveness of the developed SVM method, we compared the classification performance of SVM with three different combinations of input features, i.e. spectra only; spectra and textures; and spectra, textures, and Normalized Differential Vegetation Index (NDVI). In addition, for comparison purposes, a maximum likelihood classification method based on spectra, textures, and NDVI was adopted. Results indicate that (1) incorporation of textures and NDVI, especially textures, refines the SVM classification accuracy to 84.26%; (2) the optimal window size for texture calculation is 31 × 31 pixels; and (3) the optimal parameters of SVM model are C = 32 and γ = 8. Overall, the SVM method incorporating spectra, texture, and NDVI can accurately map salt cedar in our study site.