鍏抽敭璇�: 鍦熷湴瑕嗙洊, 鍒嗗眰鍒嗙被, 杩唬, 鍙垎鎬�, 楂樺垎浜屽彿

Abstract: 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锛宼his paper proposed a hierarchical classification and iterative CART model based method for remote sensing classification of landcover.Firstly锛宼he extraction order of land cover classes was determined based on the class separability evaluation锛寃hich was water锛寁egetation锛宐are soil and built-up land.Secondly锛寃e selected the optimal image segmentation parameters and a set of sensitive features for each class during the hierarchical classification process.Finally锛宱bject-based training samples were selected to be fed into the iterative CART algorithm for the successive extraction of the first three classes锛寃ith 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锛宎nd 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锛寃ith 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锛実iving different purposes of classification.This method can also be easily extended to other districts and disciplines involving remote sensing image classification.

Key words: Land cover, Hierarchical classification, Iteration, Separability, GF-2