ALOS Remote Sensing Classification of Vegetation Based on Different Classification Methods

doi:10.13466/j.cnki.lyzygl.2015.04.012

Abstract

Abstract: Based on the data of ALOS image of Yiliang County,Yunnan Province,this paper discusses the use of the maximum likelihood method,support vector machine method and object-oriented support vector machine(SVM).The results show that maximum like-lihood classification accuracy is 79.33%,SVM classification accuracy 82.25%,oriented object based support vector machine classification accuracy 86.13%,and oriented-object based support vector machine classification method has better classification results.The results can provide a reference for the study of high-resolution remote sensing image classification。

Key words: ALOS, maximum likelihood method, SVM, object-oriented

CLC Number:

S771.8

TENG Quanxiao, XU Tianshu. ALOS Remote Sensing Classification of Vegetation Based on Different Classification Methods[J]. FOREST RESOURCES WANAGEMENT, 2015, (4): 69-72.

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