Object-based classification for mangrove with VHR remotely sensed image

被引:0
|
作者
Liu, Zhigang [1 ,2 ]
Li, Jing [3 ]
Lim, Boonleong [4 ]
Seng, Chungyueh [4 ]
Inbaraj, Suppiah [4 ]
机构
[1] State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
[2] Beijing Normal Univ, Sch Geog & Remote Sensing Sci, Beijing 100875, Peoples R China
[3] Beijing Normal Univ, Coll Resource Sci & Technol, Beijing, Peoples R China
[4] Cilix Corp Sdn Bhd, Kuala Lumpur 57000, Malaysia
来源
GEOINFORMATICS 2007: REMOTELY SENSED DATA AND INFORMATION, PTS 1 AND 2 | 2007年 / 6752卷
基金
中国国家自然科学基金;
关键词
mangrove; SPOT-5; object-based classification;
D O I
10.1117/12.760797
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
In remotely sensed imagery with high spatial resolution, more detail spatial information of mangrove forest can be shown. It is important to find a method to effectively use the spatial information so as to improve the accuracy of mangrove forest classification. In the study, different classification schemes (including pixel-based classification and object-based classification), different classifiers, and different texture features have been conducted. The classification results of SPOT-5 image of Matang Mangrove Forest Reserve in Malaysia show that the performances of object-based classifications are better than that of pixel-based classifications. However, the classifier type is important for object-based classification. The accuracies of nearest neighborhood classifiers, which are widely used in object-based classifications, were obviously lower that that of maximum likelihood classifiers and support vector Machines. It is also shown that the involvement of second-order texture features can't effectively improve the classification accuracy of neither object-based classifications nor pixel-based classifications.
引用
收藏
页数:8
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