中国空间科学技术
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刘咏梅*，马潇，门朝光
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LIU Yongmei*，MA Xiao，MEN Chaoguang
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摘要： 在高光谱遥感图像分类方法中，空间特征和光谱特征的融合可以有效地改善分类效果。针对单一空间特征的信息表达不充分问题，提出了一种联合多种空间特征的高光谱图像空谱分类方法。利用超像素信息对分类结果进行后处理去掉椒盐噪声，并创造性地将超像素信息应用于分类前处理，提出了一种利用超像素信息对像素点的特征向量进行线性加权融合的方法。试验结果表明，所提方法的性能优于目前的通常方法。
关键词: 高光谱遥感图像分类, 空间特征, 光谱特征, 超像素, 线性加权融合
Abstract: In the field of hyperspectral remote sensing image classification, spatial and spectral feature fusion can improve the effect of classification. A multifeature spectral and spatial classification method for hyperspectral images was proposed. In the postprocessing procedure, the saltandpepper noises were removed by using superpixel information. It was also used in the preprocessing procedure, and the feature vector of pixels was weighted by superpixel information. The experiment results show that the results of proposed method are better than the present methods.
Key words: hyperspectral remote sensing image classification, spatial feature, spectral feature, superpixel, linear weighted fusion
刘咏梅，马潇，门朝光. 基于多种空间信息的高光谱遥感图像分类方法[J]. 中国空间科学技术, doi: 10.16708/j.cnki.1000-758X.2019.0013.
LIU Yongmei，MA Xiao，MEN Chaoguang. A hyperspectral remote sensing image classification method based on multispatial information[J]. Chinese Space Science and Technology, doi: 10.16708/j.cnki.1000-758X.2019.0013.
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链接本文: https://journal26.magtechjournal.com/kjkxjs/CN/10.16708/j.cnki.1000-758X.2019.0013
https://journal26.magtechjournal.com/kjkxjs/CN/Y2019/V39/I2/73
