Details

Autor(en) / Beteiligte

• Xi Chen
• Wei Liu
• Fulin Su
• Guofan Shao

Titel

Semi-supervised multiview feature selection with label learning for VHR remote sensing images

Ist Teil von

• 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016, p.2372-2375

Ort / Verlag

IEEE

Erscheinungsjahr

2016

Quelle

IEEE Xplore

Beschreibungen/Notizen

• The very high resolution (VHR) images can be seen as multiview data. For better organizing and highlighting similarities and differences between the multiple views of data, a semisupervised multiview feature selection (SemiMFS) method is proposed in this paper, based on consensus and complementary principles. In SemiMFS, feature views are generated by decomposing features into multiple disjoint and meaningful groups. Each feature group represents a view, and each view describes a data characteristic. Then features are evaluated and selected within each view. The experiments on a Worldview-2 VHR satellite image verify the effectiveness and practicability of the method, compared with traditional single-view algorithms.