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首页> 外文会议>Proceedings of the 2007 International Conference on Machine Learning and Cybernetics >MULTISPECTRAL REMOTE SENSING IMAGE CLASSIFICATION WITH MULTIPLE FEATURES
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MULTISPECTRAL REMOTE SENSING IMAGE CLASSIFICATION WITH MULTIPLE FEATURES

机译：具有多种功能的多光谱遥感图像分类

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摘要

In this paper, we propose to combine the spectral and texture features to compose the multi-feature vectors for the classification of multispectral remote sensing image.It usually is difficult to obtain the higher classification accuracy if only considers one kind feature, especially for the case of different geographical objects have the same spectrum or texture specialty for a multispectral remote sensing image.The spectral feature and the texture feature are composed together to form a new feature vector, which can represent the most effective features of the given remote sensing image.In this way we can overcome shortcomings of only using the single feature and raise the classification accuracy.The system classification performance with composed feature vector is investigated by experimentations.By analysis of results we can learn how to combine the multi-feature vector can obtain a higher classification rate, and experiments proved that the proposed method is feasible and useful in multispectral remote sensing image classification study.
机译：本文提出将光谱特征和纹理特征结合起来组成多特征向量，用于多光谱遥感图像的分类，如果仅考虑一种特征，通常很难获得较高的分类精度，特别是在这种情况下的不同地理对象对于多光谱遥感图像具有相同的光谱或纹理特征，将光谱特征和纹理特征组合在一起以形成新的特征向量，可以代表给定遥感图像的最有效特征。通过实验研究了具有组合特征向量的系统分类性能。通过对结果的分析，我们可以学习如何结合多特征向量获得更高的分类精度。分类率，并通过实验证明了该方法的可行性和实用性。 n多光谱遥感影像分类研究。

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