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The Classification Method of Multi-spectral Remote Sensing Images Based on Self-adaptive Minimum Distance Adjustment

机译:基于自适应最小距离调整的多光谱遥感图像分类方法

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摘要

The phenomenon of "Same Object with Different Spectra" in the issue of multi-spectral remote sensing images land use classification makes major effects on improving accuracy. The paper based on the analysis of modeling on classification problems, proposed a method based on minimum distance self-adaptive adjustment to realize the split of cluster centers and solved the problem of identified scope intersection leading to improving the accuracy in the classifying methods difficultly. By experiments compared with the traditional methods, it can improve classification accuracy about 4% and the results prove the validity of this method.
机译:多光谱遥感影像土地利用分类问题中的“不同光谱的同一物体”现象对提高精度有重要影响。在对分类问题进行建模分析的基础上,提出了一种基于最小距离自适应调整的聚类中心分割方法,解决了范围交叉点识别问题,难以提高分类方法的准确性。通过与传统方法的对比实验,可以将分类准确率提高约4%,结果证明了该方法的有效性。

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