A Hierarchical Classification and Iterative Model based Methodfor Remote Sensing Classification of Land Cover

Land cover classification based on remote sensing is an important means to analyze the change and spatial pattern of land use.In order to further improve the classification accuracy，this paper proposed a hierarchical classification and iterative CART model based method for remote sensing classification of landcover.Firstly，the extraction order of land cover classes was determined based on the class separability evaluation，which was water，vegetation，bare soil and built-up land.Secondly，we selected the optimal image segmentation parameters and a set of sensitive features for each class during the hierarchical classification process.Finally，object-based training samples were selected to be fed into the iterative CART algorithm for the successive extraction of the first three classes，with the remaining unclassified objects being directly assigned to the last class.Results demonstrated that the proposed method can significantly reduce the mixture between bare soil and built-up land，and is capable of achieving landcover classification with much higher accuracy.The proposed method achieved an overall accuracy of 85.76% and a Kappa efficient of 0.72，with the performance improvements ranging from 10.67% to 16.5% and 0.15 to 0.21 as compared SVM and CART single classification methods.The classification accuracy of a specific class can be flexibly adjusted using this method，giving different purposes of classification.This method can also be easily extended to other districts and disciplines involving remote sensing image classification.