Remote sensing image segmentation using geodesic-kernel functions and multi-feature spaces

Highlights：

• Compared the feature expression ability of Riemannian manifold space, spectral space and the label field. It turns out that feature expression ability of the Riemannian manifold space is the strongest.

• Explore the complementation among features expressed in different feature spaces. Experimental results show that there exists complementary information among different feature spaces and combining all the features spaces improves the segmentation accuracy significantly. Combing features in the Riemannian manifold space and the spectral space outperforms combining features in the Riemannian manifold space with the label field a little, and obtains much better results than combining the spectral space and the label field.

摘要

•Using features expressed in the Riemannian manifold space, the spectral space and the label field to propose four remote sensing image segmentation algorithms which considers the Riemannian manifold space as baseline.•Compared the feature expression ability of Riemannian manifold space, spectral space and the label field. It turns out that feature expression ability of the Riemannian manifold space is the strongest.•Explore the complementation among features expressed in different feature spaces. Experimental results show that there exists complementary information among different feature spaces and combining all the features spaces improves the segmentation accuracy significantly. Combing features in the Riemannian manifold space and the spectral space outperforms combining features in the Riemannian manifold space with the label field a little, and obtains much better results than combining the spectral space and the label field.