A Fuzzy Context Neural Network Classifier for Land Cover Classification
A Fuzzy Context Neural Network Classifier for Land Cover Classification
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DOI：
10.1109/icnc.2009.379
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发表时间：
2009-08
期刊：
影响因子：
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通讯作者：
Hao Gong;Man Zhu;Wei Li-
中科院分区：
文献类型：
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作者：
Hao Gong;Man Zhu;Wei Li-
基于统计模式识别技术的多光谱遥感数据土地覆盖分类是遥感信息提取中最常用的方法之一。在各种分类技术中，神经网络分类器对概率分布的形式没有很强的假设，并且可以根据它们所用于建模的系统的复杂性灵活地进行调整，因此被认为是一种有吸引力的选择。然而，传统的分类器通常被称为基于点或像素的分类器，因为它们仅基于其光谱特性来标记像素。在本文中，我们提出了一个新的上下文敏感的神经网络分类器，它考虑到空间上下文信息，使用模糊方法和概率标签松弛。实验结果表明，新的分类器可以减少一些孤立的错误标记，提高准确率。班级的空间一致性得到改善。
Land cover classification based on statistical pattern recognition technique applied to multispectral remote sensor data is one of the most often used methods of information extraction. Among various classification techniques, neural network classifier makes no strong assumptions about the form of the probability distributions and can be adjusted flexibly to the complexity of the system that they are being used to model, therefore considered to be an attractive choice. However, traditional classifiers are often referred to as point or pixel-based classifiers in that they label a pixel on the basis of its spectral properties alone. In this paper, we present a new context-sensitive neural network classifier, which take into account the spatial context information, using fuzzy method and probabilistic label relaxation. The experiment result shows that the new classifier can reduce some isolated mislabeling and improve the accuracy. The spatial coherence of the classes improved.