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.