| 基于蚁群算法的多光谱遥感图像分类 |
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| 引用本文： | 胡河山,覃亚丽.基于蚁群算法的多光谱遥感图像分类[J].杭州电子科技大学学报,2012,32(4):88-91. |
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| 作者姓名： | 胡河山 覃亚丽 |
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| 作者单位： | 浙江工业大学信息工程学院光纤通信与信息工程研究所,浙江杭州,310023 |
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| 摘 要： | 该文应用蚊群算法和支持向量机实现多光谱遥感图像分类.首先提取出多光谱遥感图像的光谱特征、纹理特征和形状特征,然后利用蚁群优化算法从提取出的多维特征空间中选择最优的特征子集向量,最后将特征子集作为支持向量机分类器的输入量实现分类.实验结果显示,较传统的K均值方法文章给出的方法能够提高遥感图像的分类精度.
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| 关 键 词： | 多光谱遥感图像 分类 光谱特征 形状特征 蚁群算法 支持向量机分类器 |
Classification of Multispectral Remote Sensing Image Based on ACO |
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| HU He-shan
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QIN Ya-li.Classification of Multispectral Remote Sensing Image Based on ACO[J].Journal of Hangzhou Dianzi University,2012,32(4):88-91. |
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| Authors: | HU He-shan QIN Ya-li |
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| Affiliation: | (Institute of Fiber Communication and Information Engineering, College of Information Engineering, Zhejiang University of Technology, Hangzhou Zhejiang 310023, China) |
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| Abstract: | This paper uses a port vector machine （SVM） classification method based on ant to implement multispectral remote features, textural features and shape features are extracted from colony optimization（ACO） algorithm and sup- sensing image classification. Firstly, spectral the image, and then use ACO algorithm to se- lect subset multi-feature vector from the multi-dimensional feature space. Finally, use SVM classifier to classi- fy the multispectral remote sensing image based the subset features. The experimental results show that the method presented in this paper can obtain higher classification accuracy than K-means. |
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| Keywords: | multispectral remote sensing image classification spectral feature texture feature shape fea-ture ACO algorithm SVM classifier |
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