针对原始高光谱图像信噪比较低导致的分类精度差及边缘地物光谱特征易混淆的问题,文中提出一种基于递归滤波的高光谱图像地物分类方法。首先,对原始高光谱图像进行递归滤波处理,并利用主成分分析方法降低滤波后图像的维度,消除高光谱图...针对原始高光谱图像信噪比较低导致的分类精度差及边缘地物光谱特征易混淆的问题,文中提出一种基于递归滤波的高光谱图像地物分类方法。首先,对原始高光谱图像进行递归滤波处理,并利用主成分分析方法降低滤波后图像的维度,消除高光谱图像中的大部分噪声、弱边缘和小尺度结构;然后,使用递归滤波对主成分分析后的各主成分图像进行一次滤波,在减少图像中一些更小的纹理结构的同时,避免边缘像元光谱特征的混淆;最后,将预处理后的高光谱数据发送至支持向量机分类器进行训练和预测。实验结果表明:在高光谱图像降维前后分别使用递归滤波,能更好地消除图像中的噪声和保留多尺度边缘特征;在Indian Pines和University of Pavia高光谱数据集上,所提方法的总体分类精度分别为98.17%,92.17%,相较于其他的分类方法平均提高11%和7%。展开更多
Hyperspectral remote sensing has become one of the research frontiers in ground object identification and classification. On the basis of reviewing the application of hyperspectral remote sensing in identification and...Hyperspectral remote sensing has become one of the research frontiers in ground object identification and classification. On the basis of reviewing the application of hyperspectral remote sensing in identification and classification of ground objects at home and abroad. The research results of identification and classification of forest tree species, grassland and urban land features were summarized. Then the researches of classification methods were summarized. Finally the prospects of hyperspectral remote sensing in ground object identification and classification were prospected.展开更多
文摘针对原始高光谱图像信噪比较低导致的分类精度差及边缘地物光谱特征易混淆的问题,文中提出一种基于递归滤波的高光谱图像地物分类方法。首先,对原始高光谱图像进行递归滤波处理,并利用主成分分析方法降低滤波后图像的维度,消除高光谱图像中的大部分噪声、弱边缘和小尺度结构;然后,使用递归滤波对主成分分析后的各主成分图像进行一次滤波,在减少图像中一些更小的纹理结构的同时,避免边缘像元光谱特征的混淆;最后,将预处理后的高光谱数据发送至支持向量机分类器进行训练和预测。实验结果表明:在高光谱图像降维前后分别使用递归滤波,能更好地消除图像中的噪声和保留多尺度边缘特征;在Indian Pines和University of Pavia高光谱数据集上,所提方法的总体分类精度分别为98.17%,92.17%,相较于其他的分类方法平均提高11%和7%。
文摘Hyperspectral remote sensing has become one of the research frontiers in ground object identification and classification. On the basis of reviewing the application of hyperspectral remote sensing in identification and classification of ground objects at home and abroad. The research results of identification and classification of forest tree species, grassland and urban land features were summarized. Then the researches of classification methods were summarized. Finally the prospects of hyperspectral remote sensing in ground object identification and classification were prospected.
