Polygonization of Remote Sensing Classification Maps by Mesh Approximation

Emmanuel Maggiori (1) , Yuliya Tarabalka (1) , Guillaume Charpiat (2) , Pierre Alliez (1)

1 TITANE - Geometric Modeling of 3D Environments
2 TAU - TAckling the Underspecified

The ultimate goal of land mapping from remote sensing image classification is to produce polygonal representations of Earth's objects, to be included in geographic information systems. This is most commonly performed by running a pix-elwise image classifier and then polygonizing the connected components in the classification map. We here propose a novel polygonization algorithm, which uses a labeled triangular mesh to approximate the input classification maps. The mesh is optimized in terms of an 1 norm with respect to the classifiers's output. We use a rich set of optimization operators , which includes a vertex relocator, and add a topology preservation strategy. The method outperforms current approaches , yielding better accuracy with fewer vertices.

Keywords: polygon generalization; geographic information systems; Remote sensing image classification

Domains: Computer Vision and Pattern Recognition [cs.CV]

Emmanuel Maggiori, Yuliya Tarabalka, Guillaume Charpiat, Pierre Alliez. Polygonization of Remote Sensing Classification Maps by Mesh Approximation. ICIP 2017 - IEEE International Conference on Image Processing, Sep 2017, Beijing, China. pp.5. ⟨hal-01530460⟩