优化子空间SVM集成的高光谱图像分类
Optimal subspace ensemble with SVM for hyperspectral image classification
- 2016年20卷第3期 页码:409-419
纸质出版日期: 2016
DOI: 10.11834/jrs.20165200
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纸质出版日期: 2016 ,
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[1]杨凯歌,冯学智,肖鹏峰,朱榴骏.优化子空间SVM集成的高光谱图像分类[J].遥感学报,2016,20(03):409-419.
YANG Kaige, FENG Xuezhi, XIAO Pengfeng, et al. Optimal subspace ensemble with SVM for hyperspectral image classification[J]. Journal of Remote Sensing, 2016,20(3):409-419.
随机子空间集成是很有前景的高光谱图像分类技术
子空间的多样性和单个子空间的性能与集成后的分类精度密切相关。传统方法在增强单个子空间性能的同时
往往会获得大量最优但相似的子空间
因而减小它们之间的多样性
限制集成系统的分类精度。为此
提出优化子空间SVM集成的高光谱图像分类方法。该方法采用支持向量机(SVM)作为基分类器
并通过SVM之间的模式差别对随机子空间进行k-means聚类
最后选择每类中J-M距离最大的子空间进行集成
从而实现高光谱图像分类。实验结果显示
优化子空间SVM集成的高光谱图像分类方法能够有效解决小样本情况下的Hughes效应问题;总体精度达到75%–80%
Kappa系数达到0.61–0.74;比随机子空间集成方法和随机森林方法分类精度更高、更稳定
适合高光谱图像分类。
Promoting the accuracy of hyperspectral image classification is a crucial and complex issue. Hyperspectral image provides details of spectral variation of land surface with continuous spectral data. On the one hand
this characteristic is widely utilized to analyze and interpret different land-cover classes. On the other hand
the availability of large amounts of spectral space introduces challenging methodological issues
such as curse of dimensionality. Subspace ensemble systems
such as random subspace method(RSM)
significantly outperform single classifiers in classifications involving hyperspectral image. However
two issues should be addressed to improve robustness and overall accuracy of the system. The first issue is diversity within subspace ensemble systems
and the second one is the classification accuracy of individual subspaces. In this paper
we adopt Support Vector Machine(SVM) as base classifier and proposed a novel subspace ensemble method
namely
optimal subspace SVM Ensemble
for hyperspectral image classification to improve the performance of RSM. Based on random subspace selection as the initial step
a two-step procedure is designed to avoid similarity within ensemble systems during the optimization of individual subspace accuracy. Instead of maximizing the diversity of ensemble by using a specific diversity measure
the first step employs the k-means cluster procedure according to the similarity of SVM patterns to classify random base classifiers. Second
an optimization process is implemented with Jeffries–Matusita(J-M) distance as criterion by selecting the optimal subspace from each group in the formal phase. The final label is decided based on majority voting of optimal subspaces. Experiments on two hyperspectral datasets reveal that the proposed OSSE obtains sound
robust
and overall accuracy compared with RSM and random forest method. In the first hyperspectral image
namely
the Pavia university data set
the maximum increases in Kappa coefficient and overall accuracy are about 0.04 and 2.64%
respectively
compared with those in RSM and about 0.15 and 12.75%
respectively
compared with those in random forest method. In the second hyperspectral image
namely
the Indian Pines data set
the maximum increases in Kappa coefficient and overall accuracy are about 0.02 and 1.00%
respectively
compared with those in RSM and about 0.13 and11.12%
respectively
compared with those in random forest method. The combination of optimal subspaces improves the diversity of subspace system and the accuracy of individual classifiers and thus exhibits better performance
particularly when using limited samples
which is common in hyperspectral image classification. Basing on the results of different parameter settings in OSSE
we found two interesting issues related to the number of clustering and initial size of random subspaces. First
the optimal number of clusters in OSSE is stable when using specific hyperspectral remote sensing data. Hence
the optimal number of cluster could be assessed using the characteristics of remote sensing images. Second
similar to RSM
increasing the number of random subspaces minimally contributes to the improvement of classification accuracy in OSSE. Consequently
to decrease the time cost of computing
we should avoid selecting numerous random subspaces.
高光谱图像分类随机子空间优化子空间支持向量机
hyperspectral image classificationrandom subspaceoptimal subspacesupport vector machine
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