A Dynamic Subspace Method for Hyperspectral Image Classification

作者：Yang Jinn Min^*; Kuo Bor Chen; Yu Pao Ta; Chuang Chun Hsiang

来源：IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(7): 2840-2853.

DOI：10.1109/TGRS.2010.2043533

• Kernel smoothing (KS)
• random subspace method (RSM)
• small sample size (SSS) classification

Many studies have demonstrated that multiple classifier systems, such as the random subspace method (RSM), obtain more outstanding and robust results than a single classifier on extensive pattern recognition issues. In this paper, we propose a novel subspace selection mechanism, named the dynamic subspace method (DSM), to improve RSM on automatically determining dimensionality and selecting component dimensions for diverse subspaces. Two importance distributions are proposed to impose on the process of constructing ensemble classifiers. One is the distribution of subspace dimensionality, and the other is the distribution of band weights. Based on the two distributions, DSM becomes an automatic, dynamic, and adaptive ensemble. The real data experimental results show that the proposed DSM obtains sound performances than RSM, and that the classification maps remarkably produce fewer speckles.

• 出版日期2010-7