YANG Kaige, FENG Xuezhi, XIAO Pengfeng, et al. Optimal subspace ensemble with SVM for hyperspectral image classification. [J]. Journal of Remote Sensing 20(3):409-419(2016)
YANG Kaige, FENG Xuezhi, XIAO Pengfeng, et al. Optimal subspace ensemble with SVM for hyperspectral image classification. [J]. Journal of Remote Sensing 20(3):409-419(2016) DOI: 10.11834/jrs.20165200.
Optimal subspace ensemble with SVM for hyperspectral image classification
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.