融合像素—多尺度区域特征的高分辨率遥感影像分类算法
Fusion of pixel-based and multi-scale region-based features for the classification of high-resolution remote sensing image
- 2015年19卷第2期 页码:228-239
纸质出版日期: 2015
DOI: 10.11834/jrs.20154035
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纸质出版日期: 2015 ,
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[1]刘纯,洪亮,陈杰,楚森森,邓敏.融合像素—多尺度区域特征的高分辨率遥感影像分类算法[J].遥感学报,2015,19(02):228-239.
LIU Chun, HONG Liang, CHEN Jie, et al. Fusion of pixel-based and multi-scale region-based features for the classification of high-resolution remote sensing image[J]. Journal of Remote Sensing, 2015,19(2):228-239.
针对基于像素多特征的高分辨率遥感影像分类算法的"胡椒盐"现象和面向对象影像分析方法的"平滑地物细节"现象
提出了一种融合像素特征和多尺度区域特征的高分辨率遥感影像分类算法。(1)首先采用均值漂移算法对原始影像进行初始过分割
然后对初始过分割结果进行多尺度的区域合并
形成多尺度分割结果。根据多尺度区域合并RMI指数变化和分割尺度对分类精度的影响
确定最优分割尺度。(2)融合光谱特征、像元形状指数PSI(Pixel Shape Index)、初始尺度和最优尺度区域特征
并对多类型特征进行归一化
最后结合支持向量机(SVM)进行分类。实验结果表明该算法既能有效减少基于像素多特征的高分辨率遥感影像分类算法的"胡椒盐"现象
又能保持地物对象的完整性和地物细节信息
提高易混淆类别(如阴影和街道
裸地和草地)的分类精度。
With the improvement of spatial resolution of remote sensing image
the details
geometrical structure and texture features of ground objects have been better presented. As the same object type has different spectra or different object types have same spectrum
the statistical separability of different land cover classes in spectral domain is reduced
which is a great challenge to the traditional classification methods based on pixel-features for high spatial resolution remote sensing image. Classification accuracies based on pixel classification methods are improved by fusing pixel texture
structure and shape features. But the pixel-based multi-feature classification methods generally have the shortcomings of"salt and pepper"effect and computational complexity. In recent years
the Object Based Image Analysis( OBIA) method has been widely concerned. The basic characteristic of OBIA is homogeneous regions as processing units. OBIA method can solve"salt and pepper"problem within traditional methods
and overcomes the shortcomings among pixel-based classification methods. However
a large segmentation scale in OBIA leads to lose detail and present"excessive smoothing"phenomenon. In view of the"salt and pepper"phenomenon of pixel-based multi-feature classification methods and the "excessive smoothing "phenomenon of OBIA
a classification method which fused pixel-based multifeature and multi-scale region-based features is proposed in this paper.( 1) The over-segment image objects are obtained by mean shift algorithm. Then regions are merged based on the original over-segmentation results through multi-scale
and the multi-scale segmentation results are obtained. According to change of multi-scale regions merged index-RMI and the correlation between classification accuracy and segmentation scale
when the RMI change is small
the adjacent regions are merged
and the RMI change is significant
best segmentation results are obtained in the optimal scale and the adjacent regions merging processes are stopped. The correlation among segmentation scales
segmentation numbers and OA is analyzed. Finally
the optimal segmentation scale is determined.( 2) Spectral features
shape features and multi-scale region features are extracted
then spectral features
Pixel Shape Index( PSI) features and region features of the original scale and the optimal scale are fused
and features of various types are normalized. Finally
the classification is implemented by Support Vector Machine( SVM). In order to test the effect of the proposed method discussed in this paper
two high spatial resolution hyper spectral remote sensing images are adopted. Some A series of experiment schemes are designed
which include classification methods using pixel-based LBP
GLCM and PSI features
Object-Based Image Analysis( OBIA)
and single scale segmentation results by e Cognition algorithms and Meanshift( MS). Classification results are evaluated by quantitative and qualitative methods
which. are confusion matrix
Overall Accuracy( OA) and Kappa coefficient as quantitative evaluation and visual discrimination as qualitative evaluation. The classification accuracy of the proposed method is higher than pixel-based multi-feature methods
OBIA method in e Cogniton software and single scale classification results based on MS segmentation method. The experiment results show that the proposed method can effectively take advantages and reduce disadvantages of pixel-based and region-based classification methods and improve classification accuracies of different land cover classes.
高分辨率遥感影像融合多尺度像元形状指数支持向量机
high resolution remote sensing imagefusionmulti-scalePSISVM
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