Main content area

Manifold based on neighbour mapping and its projection for remote sensing image segmentation

Zhao, Xuemei, Li, Yu, Wang, Haijian
International journal of remote sensing 2019 v.40 no.24 pp. 9304-9320
algorithms, normal distribution, remote sensing
To accurately describe the features of a remote sensing image by considering the relationship in the neighbourhood system, this paper presents a neighbour mapping and manifold projection-based image segmentation algorithm called NM_MP (Neighbour Mapping and Manifold Projection, NM_MP). First, the features of the image are described by Gaussian distributions. Then, the image described by the Gaussian distributions is mapped to a manifold called the Riemannian manifold that is able to characterize the patterns of objects in remote sensing images. To fully utilize the advantages of the expression ability of the Riemannian manifold space, a data submanifold and a parameter submanifold are established to depict the features of the image and the corresponding segmentation result. Through projecting points from the data submanifold onto the nearest candidate on the parameter submanifold and updating the candidates according to the projection results, the candidates tend to approach optimum segmentation. The NM_MP algorithm is validated on synthetic and real remote sensing images. The experimental analysis demonstrates that the NM_MP algorithm can effectively decrease the impact of noise and outliers and consequently obtain promising segmentation results on remote sensing images.