Study of Remote Sensing Image Classification Based on Spatial Data Mining Techniques
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摘要: 采用数据发掘技术从 GIS数据库和遥感图像中发现知识,用于改善遥感图像分类。提出了两种实施空间数据归纳学习的途径:在空间对象粒度上学习和直接在像元粒度上学习。分析了两种粒度学习的特点和适用范围,同时提出了一种归纳学习与传统图像分类法的结合方式。用北京地区 SPOT多光谱图像和 GIS数据库进行土地利用分类的试验证明,归纳学习能较好地解决同谱异物、同物异谱等问题,显著提高分类精度,并且能够根据发现的知识进一步细分类,扩展了遥感图像分类的能力。Abstract: Data mining techniques are ed to discover knowledge from GIS database and remote sensing image data in order to improve image classification. Two learning granularities are proposed for inductive learning from spatial data. One is spatial object granuldrity and the other is pixel granularity. The characteristics and application scope of the two granularities are discussed. We also present an approach to combine inductive learning with conventional image classification methods, which selects class probability of Bayes classification as learning attributes. A land use classification experiment is performed in the Beijing area using SPOT multi-spectral image and GIS data. Rules about spatial distribution patterns and geometric features are discovered by C5.0 inductive learning algorithm and then the image is reclassified by deductive reasoning.Compared with the result produced only by Bayes classification, the overall accuracy increased 11 percent and the accuracy of some classes, such as garden and forest, increased about 30 percent. The results indicate that inductive learning can resolve spectral confusion to a great extent. Combining Bayes method with inductive learning not only improves classification accuracy greatly, but also extends the classification by subdividing some classes with the discovered knowledge.
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