Fuzzy nonlinear proximal support vector machine for land extraction based on remote sensing image.
Currently, remote sensing technologies were widely employed in the dynamic monitoring of the land. This paper presented an algorithm named fuzzy nonlinear proximal support vector machine (FNPSVM) by basing on ETM(+) remote sensing image. This algorithm is applied to extract various types of lands of the city Da'an in northern China. Two multi-category strategies, namely "one-against-one" and "one-against-rest" for this algorithm were described in detail and then compared. A fuzzy membership function was presented to reduce the effects of noises or outliers on the data samples. The approaches of feature extraction, feature selection, and several key parameter settings were also given. Numerous experiments were carried out to evaluate its performances including various accuracies (overall accuracies and kappa coefficient), stability, training speed, and classification speed. The FNPSVM classifier was compared to the other three classifiers including the maximum likelihood classifier (MLC), back propagation neural network (BPN), and the proximal support vector machine (PSVM) under different training conditions. The impacts of the selection of training samples, testing samples and features on the four classifiers were also evaluated in these experiments.
Tianjin Chengjian University, Tianjin, China.
Xiaomei
X
Zhong
Jianping
J
Li
Huacheng
H
Dou
Shijun
S
Deng
Guofei
G
Wang
Yu
Y
Jiang
Yongjie
Y
Wang
Zebing
Z
Zhou
Li
L
Wang
Fei
F
Yan
D002681:China; D017143:Fuzzy Logic; D005843:Geography; D017711:Nonlinear Dynamics; D058998:Remote Sensing Technology; D060388:Support Vector Machine