首页> 外文会议>Conference on Data Mining and Applications Oct 23-24, 2001, Wuhan, China >Multi-source Remote Sensing Data Fusion Using Fuzzy Self-organization Mapping Network and Modified Dempster-Shafer Evidential Reasoning Method to Classification
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Multi-source Remote Sensing Data Fusion Using Fuzzy Self-organization Mapping Network and Modified Dempster-Shafer Evidential Reasoning Method to Classification

机译:基于模糊自组织映射网络和改进的Dempster-Shafer证据推理方法的多源遥感数据融合分类

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摘要

By integrating Fuzzy Kohonen Clustering Network (FKCN) with Fuzzy Dempster-Shafer Evidential Reasoning Theory (FDSERT), a new multi-source data fusion of Remote Sensing information algorithm is proposed in this paper. The new algorithm can be applied in classification of remote sensing image through FKCN learning and FDSERT fusing. Experimental results comparing with the FKCN algorithm indicates that the classification algorithm of multi-source data fusion of Remote Sensing is superior to that of FKCN algorithm. And the algorithm can obviously improve classification accuracy. At the same time, the algorithm can makes the best of expert knowledge. Therefore the algorithm is an effective classification algorithm of Remote Sensing image.
机译:通过将模糊Kohonen聚类网络(FKCN)与模糊Dempster-Shafer证据推理理论(FDSERT)集成,提出了一种新的遥感信息算法多源数据融合方法。通过FKCN学习和FDSERT融合可以将新算法应用于遥感图像分类。实验结果与FKCN算法的比较表明,遥感多源数据融合的分类算法优于FKCN算法。该算法可以明显提高分类精度。同时,该算法可以充分利用专家知识。因此,该算法是一种有效的遥感图像分类算法。

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