Details

Autor(en) / Beteiligte

• YANG, Jinn-Min
• KUO, Bor-Chen
• YU, Pao-Ta
• CHUANG, Chun-Hsiang

Titel

A Dynamic Subspace Method for Hyperspectral Image Classification

Ist Teil von

• IEEE transactions on geoscience and remote sensing, 2010-07, Vol.48 (7), p.2840-2853

Ort / Verlag

New York, NY: IEEE

Erscheinungsjahr

2010

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Xplore

Beschreibungen/Notizen

• 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.