Tianjin Chengjian University, Tianjin, China.
PLoS One. 2013 Jul 23;8(7):e69434. doi: 10.1371/journal.pone.0069434. Print 2013.
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.
目前，遥感技术已广泛应用于土地动态监测。本文提出了一种基于 ETM(+)遥感图像的模糊非线性近支持向量机（FNPSVM）算法。该算法应用于提取中国北方大安市的各种类型土地。详细描述了两种多类策略，即“一对一”和“一对多”，并对其进行了比较。提出了一种模糊隶属度函数来减少噪声或异常值对数据样本的影响。还给出了特征提取、特征选择和几个关键参数设置的方法。进行了大量实验来评估其性能，包括各种精度（总体精度和kappa 系数）、稳定性、训练速度和分类速度。在不同的训练条件下，将 FNPSVM 分类器与最大似然分类器（MLC）、反向传播神经网络（BPN）和近支持向量机（PSVM）进行了比较。还评估了训练样本、测试样本和特征的选择对这四个分类器的影响。
